添加MAP MCP、联网搜索skill

2026-03-27 17:17:29 +08:00
parent 90784a2f7d
commit 6fc17c81f0
21 changed files with 4281 additions and 16 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,6 @@
+# customize your .gitignore as needed
+nanobot-config/workspace/sessions/*
+
 # ---> Python
 # Byte-compiled / optimized / DLL files
 __pycache__/
--- a/6
+++ b/6
@@ -6,7 +6,11 @@ ENV PATH="/root/.local/bin:${PATH}"
 RUN uv tool install --upgrade nanobot-ai -i https://pypi.tuna.tsinghua.edu.cn/simple

 # Persist nanobot config/workspace here via volume mount.
-RUN mkdir -p /root/.nanobot
+RUN mkdir -p /root/.nanobot /opt/mcp
+
+# Preinstall local MCP project dependencies into the image.
+COPY MCP /opt/mcp
+RUN uv pip install --system -e /opt/mcp/mars-mcp -e /opt/mcp/map-mcp -i https://pypi.tuna.tsinghua.edu.cn/simple

 WORKDIR /workspace

--- a/MCP/map-mcp/.gitignore
+++ b/MCP/map-mcp/.gitignore
@@ -0,0 +1,138 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
--- a/MCP/map-mcp/pyproject.toml
+++ b/MCP/map-mcp/pyproject.toml
@@ -0,0 +1,16 @@
+[project]
+name = "map-mcp"
+version = "0.1.0"
+description = "地图操作 MCP 服务器 - 通过 WebSocket 桥接 nanobot 与 Cesium 前端"
+requires-python = ">=3.12"
+dependencies = [
+    "mcp[cli]>=1.0.0",
+    "websockets>=15.0",
+]
+
+[tool.hatch.build.targets.wheel]
+packages = ["."]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
--- a/MCP/map-mcp/server.py
+++ b/MCP/map-mcp/server.py
@@ -0,0 +1,233 @@
+#!/usr/bin/env python3
+"""Map-MCP: 地图操作 MCP 服务器
+
+通过 stdio 与 nanobot 通信 (MCP 协议)，
+同时运行 WebSocket 服务器与浏览器中的 Cesium 前端通信。
+
+架构:
+  nanobot ←stdio→ Map-MCP ←WebSocket→ 浏览器(Cesium)
+"""
+
+import asyncio
+import json
+import logging
+import os
+import uuid
+from contextlib import asynccontextmanager
+
+import websockets
+
+from mcp.server.fastmcp import FastMCP
+
+# ─── 配置 ─────────────────────────────────────────────────────────────────────
+WS_HOST = os.getenv("MAP_MCP_WS_HOST", "0.0.0.0")
+WS_PORT = int(os.getenv("MAP_MCP_WS_PORT", "8005"))
+
+logger = logging.getLogger("map-mcp")
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(name)s] %(levelname)s: %(message)s",
+)
+
+# ─── WebSocket 状态 ───────────────────────────────────────────────────────────
+_clients: set[websockets.ServerConnection] = set()
+_pending: dict[str, asyncio.Future] = {}
+
+
+async def _ws_handler(websocket):
+    """处理浏览器 WebSocket 连接"""
+    _clients.add(websocket)
+    logger.info("浏览器已连接 (共 %d 个客户端)", len(_clients))
+    try:
+        async for raw in websocket:
+            try:
+                data = json.loads(raw)
+            except json.JSONDecodeError:
+                logger.warning("收到无效 JSON: %s", raw[:200])
+                continue
+            req_id = data.get("id")
+            if req_id and req_id in _pending:
+                _pending[req_id].set_result(data)
+    except websockets.ConnectionClosed:
+        pass
+    finally:
+        _clients.discard(websocket)
+        logger.info("浏览器断开 (共 %d 个客户端)", len(_clients))
+
+
+async def _send_cmd(cmd: str, params: dict | None = None, timeout: float = 15.0) -> dict:
+    """向浏览器发送命令并等待响应"""
+    if not _clients:
+        return {"ok": False, "error": "没有浏览器连接，请先打开地图页面"}
+
+    req_id = str(uuid.uuid4())
+    msg = {"id": req_id, "cmd": cmd}
+    if params:
+        msg.update(params)
+
+    loop = asyncio.get_running_loop()
+    future = loop.create_future()
+    _pending[req_id] = future
+
+    # 发送给第一个连接的客户端
+    client = next(iter(_clients))
+    await client.send(json.dumps(msg, ensure_ascii=False))
+
+    try:
+        result = await asyncio.wait_for(future, timeout=timeout)
+        return result
+    except asyncio.TimeoutError:
+        return {"ok": False, "error": f"浏览器响应超时 ({timeout}s)"}
+    finally:
+        _pending.pop(req_id, None)
+
+
+# ─── FastMCP 生命周期：启动 WebSocket 服务器 ──────────────────────────────────
+@asynccontextmanager
+async def lifespan(server):
+    ws_server = await websockets.serve(_ws_handler, WS_HOST, WS_PORT)
+    logger.info("WebSocket 服务器已启动: ws://%s:%d", WS_HOST, WS_PORT)
+    try:
+        yield
+    finally:
+        ws_server.close()
+        await ws_server.wait_closed()
+        logger.info("WebSocket 服务器已关闭")
+
+
+mcp = FastMCP("map-mcp", lifespan=lifespan)
+
+
+# ─── MCP 工具定义 ─────────────────────────────────────────────────────────────
+
+@mcp.tool()
+async def map_zoom_to(lon: float, lat: float, altitude: float = 500000.0) -> str:
+    """飞行到指定火星坐标位置。
+
+    Args:
+        lon: 经度 (火星坐标, -180 ~ 180)
+        lat: 纬度 (火星坐标, -90 ~ 90)
+        altitude: 相机高度，单位米 (默认 500km，适合查看区域全貌。近距离查看可用 50000~100000)
+    """
+    result = await _send_cmd("zoom_to", {"lon": lon, "lat": lat, "altitude": altitude})
+    if result.get("ok"):
+        return f"已飞行到 经度{lon}°, 纬度{lat}°, 高度{altitude/1000:.0f}km"
+    return f"操作失败: {result.get('error', '未知错误')}"
+
+
+@mcp.tool()
+async def map_get_viewport() -> str:
+    """获取当前地图视野信息，包括相机位置（经纬度、高度）和可视范围边界框。
+
+    返回 JSON:
+    {
+        "center": {"lon": ..., "lat": ..., "altitude": ...},
+        "bbox": {"west": ..., "south": ..., "east": ..., "north": ...}
+    }
+    """
+    result = await _send_cmd("get_viewport")
+    if result.get("ok"):
+        return json.dumps(result.get("data", {}), ensure_ascii=False)
+    return f"获取失败: {result.get('error', '未知错误')}"
+
+
+@mcp.tool()
+async def map_draw_bbox(
+    west: float, south: float, east: float, north: float,
+    color: str = "#ff6600",
+) -> str:
+    """在地图上绘制矩形边界框并飞行到该区域。
+
+    Args:
+        west: 西边界经度
+        south: 南边界纬度
+        east: 东边界经度
+        north: 北边界纬度
+        color: 边框颜色 (CSS 颜色值, 默认橙色)
+    """
+    result = await _send_cmd("draw_bbox", {
+        "bbox": {"west": west, "south": south, "east": east, "north": north},
+        "color": color,
+    })
+    if result.get("ok"):
+        return f"已绘制矩形: ({west},{south}) - ({east},{north})"
+    return f"绘制失败: {result.get('error', '未知错误')}"
+
+
+@mcp.tool()
+async def map_spatial_query(product: str = "tianwen_moric") -> str:
+    """在当前绘制的矩形区域内查询火星遥感影像数据，结果会显示在前端界面。
+
+    必须先调用 map_draw_bbox 绘制查询区域，然后再调用此工具执行查询。
+
+    Args:
+        product: 数据产品类型，可选值:
+            tianwen_moric - 天问中分相机
+            tianwen_hiric - 天问高分相机
+            mars_ctx - Mars CTX
+            mars_hirise - Mars HiRISE
+    """
+    result = await _send_cmd("spatial_query", {"product": product}, timeout=30.0)
+    if result.get("ok"):
+        count = result.get("count", 0)
+        items = result.get("items", [])
+        if count == 0:
+            return "查询完成，未找到匹配的影像数据。可尝试扩大查询范围或更换产品类型。"
+        summary = f"查询完成，找到 {count} 条影像数据，已在前端结果面板中显示。\n\n"
+        for i, item in enumerate(items[:5], 1):
+            name = item.get("name", "N/A")
+            summary += f"{i}. {name} ({item.get('product', '')})\n"
+        if count > 5:
+            summary += f"\n... 还有 {count - 5} 条结果"
+        return summary
+    return f"查询失败: {result.get('error', '未知错误')}"
+
+
+@mcp.tool()
+async def map_render_footprints(geojson: str) -> str:
+    """将 GeoJSON FeatureCollection 渲染到地图上，用于显示影像覆盖范围。
+
+    每个 Feature 的 geometry 应为 Polygon 类型，properties 中可包含 id、filename 等信息。
+
+    Args:
+        geojson: GeoJSON 字符串 (FeatureCollection 格式)
+    """
+    try:
+        data = json.loads(geojson) if isinstance(geojson, str) else geojson
+    except json.JSONDecodeError:
+        return "GeoJSON 解析失败，请检查格式"
+
+    result = await _send_cmd("render_footprints", {"geojson": data})
+    if result.get("ok"):
+        count = result.get("count", len(data.get("features", [])))
+        return f"已渲染 {count} 个覆盖范围到地图"
+    return f"渲染失败: {result.get('error', '未知错误')}"
+
+
+@mcp.tool()
+async def map_clear() -> str:
+    """清除地图上所有由 AI 绘制的内容（边界框、覆盖范围等），不影响用户手绘内容。"""
+    result = await _send_cmd("clear")
+    if result.get("ok"):
+        return "已清除地图上的 AI 绘制内容"
+    return f"清除失败: {result.get('error', '未知错误')}"
+
+
+@mcp.tool()
+async def map_get_drawn_geometry() -> str:
+    """获取用户在地图上手绘的几何图形，可用于确定查询范围。
+
+    返回 JSON:
+    {
+        "bbox": {"minLon": ..., "maxLon": ..., "minLat": ..., "maxLat": ...},
+        "hasGeometry": true/false
+    }
+    """
+    result = await _send_cmd("get_drawn_geometry")
+    if result.get("ok"):
+        return json.dumps(result.get("data", {}), ensure_ascii=False)
+    return f"获取失败: {result.get('error', '未知错误')}"
+
+
+if __name__ == "__main__":
+    mcp.run()
--- a/MCP/map-mcp/uv.lock
+++ b/MCP/map-mcp/uv.lock
@@ -0,0 +1,735 @@
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+    { url = "https://files.pythonhosted.org/packages/6f/28/258ebab549c2bf3e64d2b0217b973467394a9cea8c42f70418ca2c5d0d2e/websockets-16.0-py3-none-any.whl", hash = "sha256:1637db62fad1dc833276dded54215f2c7fa46912301a24bd94d45d46a011ceec", size = 171598, upload-time = "2026-01-10T09:23:45.395Z" },
+]
--- a/MCP/mars-mcp/pyproject.toml
+++ b/MCP/mars-mcp/pyproject.toml
@@ -0,0 +1,12 @@
+[project]
+name = "mars-mcp"
+version = "0.1.0"
+description = "火星数据 MCP 服务器 - 为 nanobot 提供时空查询工具"
+requires-python = ">=3.12"
+dependencies = [
+    "mcp[cli]>=1.0.0",
+    "chromadb>=1.4.1",
+    "requests>=2.32.5",
+    "numpy>=1.26.0",
+    "python-dotenv>=1.0.0",
+]
--- a/MCP/mars-mcp/server.py
+++ b/MCP/mars-mcp/server.py
@@ -0,0 +1,312 @@
+#!/usr/bin/env python3
+"""
+火星数据 MCP 服务器
+为 nanobot 提供时空查询和时空-语义查询工具
+"""
+import json
+import logging
+import os
+from pathlib import Path
+
+import numpy as np
+import requests
+import chromadb
+from dotenv import load_dotenv
+from mcp.server.fastmcp import FastMCP
+
+# 加载环境变量 - 优先本地 .env，回退到 mars-data-serv-llm 项目的 .env
+_here = Path(__file__).parent
+for _env in [_here / ".env", Path("~/studio/mars-data-serv-llm/.env").expanduser()]:
+    if _env.exists():
+        load_dotenv(_env)
+        break
+
+DATACUBE_API_URL = os.getenv(
+    "DATACUBE_API_URL",
+    "http://digitalmars.com.cn/datacube/datasets/search"
+)
+EMBEDDING_API_URL = os.getenv(
+    "EMBEDDING_API_URL",
+    "http://192.168.190.42:11434/api/embeddings"
+)
+EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "qwen3-embedding:8b-fp16")
+CHROMADB_PATH = os.getenv(
+    "CHROMADB_PATH",
+    str(Path("~/studio/mars-data-serv-llm/data/chromadb").expanduser())
+)
+
+logging.basicConfig(level=logging.WARNING)
+logger = logging.getLogger(__name__)
+
+mcp = FastMCP("mars-datacube")
+
+
+# ─── 内部工具函数 ─────────────────────────────────────────────────────────────
+
+def _validate_bbox(lat_min, lat_max, lon_min, lon_max):
+    """验证边界框参数，返回错误字符串或 None"""
+    if not (-90 <= lat_min <= 90) or not (-90 <= lat_max <= 90):
+        return "纬度必须在-90到90之间"
+    if not (-180 <= lon_min <= 180) or not (-180 <= lon_max <= 180):
+        return "经度必须在-180到180之间"
+    if lat_min >= lat_max or lon_min >= lon_max:
+        return "最小值必须小于最大值"
+    return None
+
+
+def _parse_item(item: dict) -> dict:
+    """从 DataCube API 响应条目中提取标准化字段"""
+    image_path = item.get("image_urls", {}).get("red", "")
+    filename = image_path.split("/")[-1] if image_path else ""
+
+    coords = item.get("coordinates", [[]])
+    if coords and len(coords[0]) > 0:
+        lons = [p[0] for p in coords[0]]
+        lats = [p[1] for p in coords[0]]
+        coord_range = {
+            "lon_min": min(lons), "lon_max": max(lons),
+            "lat_min": min(lats), "lat_max": max(lats),
+        }
+    else:
+        coord_range = None
+
+    return {
+        "id": item.get("id", ""),
+        "filename": filename,
+        "product": item.get("product", ""),
+        "image_path": image_path,
+        "coordinates": coord_range,
+        "time": str(item.get("time", "")) or "未知时间",
+    }
+
+
+def _spatial_query(lat_min, lat_max, lon_min, lon_max, product) -> list:
+    """调用 DataCube API 进行空间查询，返回原始条目列表"""
+    params = {
+        "min_lon": lon_min, "max_lon": lon_max,
+        "min_lat": lat_min, "max_lat": lat_max,
+        "product": product,
+    }
+    response = requests.get(DATACUBE_API_URL, params=params, timeout=30)
+    response.raise_for_status()
+    return response.json() or []
+
+
+def _get_embedding(text: str):
+    """调用 Embedding API，返回向量列表，失败返回 None"""
+    try:
+        resp = requests.post(
+            EMBEDDING_API_URL,
+            json={"model": EMBEDDING_MODEL, "prompt": text},
+            timeout=30,
+        )
+        resp.raise_for_status()
+        return resp.json()["embedding"]
+    except Exception as e:
+        logger.error(f"Embedding API 请求失败: {e}")
+        return None
+
+
+def _cosine_similarity(vec1, vec2) -> float:
+    v1, v2 = np.array(vec1), np.array(vec2)
+    norm = np.linalg.norm(v1) * np.linalg.norm(v2)
+    return float(np.dot(v1, v2) / norm) if norm else 0.0
+
+
+def _get_chroma_collection():
+    client = chromadb.PersistentClient(path=CHROMADB_PATH)
+    return client.get_or_create_collection("mars_images")
+
+
+# ─── MCP 工具定义 ─────────────────────────────────────────────────────────────
+
+# @mcp.tool()
+# def spatiotemporal_search(
+#     lat_min: float,
+#     lat_max: float,
+#     lon_min: float,
+#     lon_max: float,
+#     product: str = "tianwen_moric",
+#     top_k: int = 10,
+# ) -> str:
+#     """
+#     按空间范围查询火星遥感影像。
+
+#     返回指定经纬度矩形区域内的影像列表，包含影像 ID、文件路径、
+#     坐标范围和拍摄时间。适用于"查找某区域有哪些影像"类问题。
+
+#     Args:
+#         lat_min: 最小纬度 (-90 到 90)
+#         lat_max: 最大纬度 (-90 到 90)
+#         lon_min: 最小经度 (-180 到 180)
+#         lon_max: 最大经度 (-180 到 180)
+#         product: 产品类型，默认 "tianwen_moric"（天问一号MoRIC）
+#         top_k: 返回数量上限，默认 10，最大 100
+#     """
+#     err = _validate_bbox(lat_min, lat_max, lon_min, lon_max)
+#     if err:
+#         return json.dumps({"success": False, "error": err, "results": []}, ensure_ascii=False)
+
+#     top_k = min(max(1, top_k), 100)
+
+#     try:
+#         raw = _spatial_query(lat_min, lat_max, lon_min, lon_max, product)
+
+#         if not raw:
+#             return json.dumps({
+#                 "success": True,
+#                 "query": {"spatial": {"lat_min": lat_min, "lat_max": lat_max, "lon_min": lon_min, "lon_max": lon_max}, "product": product},
+#                 "total_results": 0,
+#                 "results": [],
+#                 "message": f"在指定区域未找到 {product} 影像",
+#             }, ensure_ascii=False, indent=2)
+
+#         results = [_parse_item(item) for item in raw[:top_k]]
+
+#         return json.dumps({
+#             "success": True,
+#             "query": {"spatial": {"lat_min": lat_min, "lat_max": lat_max, "lon_min": lon_min, "lon_max": lon_max}, "product": product},
+#             "total_results": len(raw),
+#             "returned_results": len(results),
+#             "results": results,
+#             "message": f"找到 {len(raw)} 幅 {product} 影像，返回前 {len(results)} 幅",
+#         }, ensure_ascii=False, indent=2)
+
+#     except requests.exceptions.RequestException as e:
+#         return json.dumps({"success": False, "error": f"DataCube API 请求失败: {e}", "results": []}, ensure_ascii=False)
+#     except Exception as e:
+#         logger.exception("spatiotemporal_search 失败")
+#         return json.dumps({"success": False, "error": str(e), "results": []}, ensure_ascii=False)
+
+
+@mcp.tool()
+def spatiotemporal_semantic_search(
+    query: str,
+    lat_min: float,
+    lat_max: float,
+    lon_min: float,
+    lon_max: float,
+    product: str = "tianwen_moric",
+    top_k: int = 10,
+) -> str:
+    """
+    时空-语义搜索：先按空间范围筛选影像，再按语义相似度排序。
+
+    先从 DataCube API 获取区域内所有影像，再从 ChromaDB 取出这些影像的
+    视觉描述向量，与 query 计算余弦相似度后排序，返回最相关的影像。
+    适用于"在某区域找尘卷风/撞击坑/沙丘"等含语义内容的问题。
+
+    结果额外包含：
+    - description: 影像视觉描述文本
+    - similarity: 与 query 的余弦相似度 (0-1)
+
+    Args:
+        query: 语义查询文本，如 "尘卷风"、"撞击坑"、"沙丘"
+        lat_min: 最小纬度 (-90 到 90)
+        lat_max: 最大纬度 (-90 到 90)
+        lon_min: 最小经度 (-180 到 180)
+        lon_max: 最大经度 (-180 到 180)
+        product: 产品类型，默认 "tianwen_moric"
+        top_k: 返回数量上限，默认 10，最大 100
+    """
+    if not query or not query.strip():
+        return json.dumps({"success": False, "error": "语义查询不能为空", "results": []}, ensure_ascii=False)
+
+    err = _validate_bbox(lat_min, lat_max, lon_min, lon_max)
+    if err:
+        return json.dumps({"success": False, "error": err, "results": []}, ensure_ascii=False)
+
+    top_k = min(max(1, top_k), 100)
+
+    query_info = {
+        "spatial": {"lat_min": lat_min, "lat_max": lat_max, "lon_min": lon_min, "lon_max": lon_max},
+        "product": product,
+    }
+
+    try:
+        # 步骤 1：空间筛选
+        raw = _spatial_query(lat_min, lat_max, lon_min, lon_max, product)
+
+        if not raw:
+            return json.dumps({
+                "success": True, "query": query_info,
+                "total_results": 0, "results": [],
+                "message": f"在指定区域未找到 {product} 影像",
+            }, ensure_ascii=False, indent=2)
+
+        # 步骤 2：构建文件名索引
+        spatial_map = {}
+        for item in raw:
+            parsed = _parse_item(item)
+            if parsed["filename"]:
+                spatial_map[parsed["filename"]] = parsed
+
+        # 步骤 3：从 ChromaDB 批量取向量
+        try:
+            collection = _get_chroma_collection()
+            chroma_data = collection.get(
+                ids=list(spatial_map.keys()),
+                include=["documents", "embeddings"],
+            )
+        except Exception as e:
+            logger.error(f"ChromaDB 查询失败: {e}")
+            results = list(spatial_map.values())[:top_k]
+            return json.dumps({
+                "success": True, "query": query_info,
+                "total_results": len(spatial_map),
+                "returned_results": len(results),
+                "results": results,
+                "message": f"ChromaDB 不可用，返回 {len(results)} 幅时空搜索结果",
+                "warning": str(e),
+            }, ensure_ascii=False, indent=2)
+
+        if not chroma_data or not chroma_data["ids"]:
+            results = list(spatial_map.values())[:top_k]
+            return json.dumps({
+                "success": True, "query": query_info,
+                "total_results": len(spatial_map),
+                "returned_results": len(results),
+                "results": results,
+                "message": f"区域内 {len(spatial_map)} 幅影像在 ChromaDB 中无语义数据",
+                "warning": "ChromaDB 中没有这些影像的语义向量",
+            }, ensure_ascii=False, indent=2)
+
+        # 步骤 4：生成查询向量并排序
+        query_vec = _get_embedding(query)
+
+        scored = []
+        for i, fname in enumerate(chroma_data["ids"]):
+            if fname not in spatial_map:
+                continue
+            item = dict(spatial_map[fname])
+            item["description"] = chroma_data["documents"][i]
+
+            if query_vec is not None and chroma_data["embeddings"] is not None:
+                item["similarity"] = round(_cosine_similarity(query_vec, chroma_data["embeddings"][i]), 4)
+            else:
+                item["similarity"] = 0.0
+            scored.append(item)
+
+        scored.sort(key=lambda x: x["similarity"], reverse=True)
+        final = scored[:top_k]
+
+        return json.dumps({
+            "success": True, "query": query_info,
+            "total_results": len(final),
+            "returned_results": len(final),
+            "results": final,
+            "message": (
+                f"区域内 {len(spatial_map)} 幅影像，其中 {len(chroma_data['ids'])} 幅有语义数据，"
+                f"按 '{query}' 排序后返回前 {len(final)} 幅"
+            ),
+        }, ensure_ascii=False, indent=2)
+
+    except requests.exceptions.RequestException as e:
+        return json.dumps({"success": False, "error": f"DataCube API 请求失败: {e}", "results": []}, ensure_ascii=False)
+    except Exception as e:
+        logger.exception("spatiotemporal_semantic_search 失败")
+        return json.dumps({"success": False, "error": str(e), "results": []}, ensure_ascii=False)
+
+
+if __name__ == "__main__":
+    mcp.run()
--- a/MCP/mars-mcp/uv.lock
+++ b/MCP/mars-mcp/uv.lock
--- a/README.md
+++ b/README.md
@@ -1,3 +1,10 @@
 # mars-nanobot

-mars-nanobot配置文件。
+mars-nanobot配置文件。
+
+## Docker构建
+
+```bash
+sudo docker compose build
+docker compose up -d nanobot-gateway
+```
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -8,8 +8,10 @@ services:
    restart: unless-stopped
    volumes:
      - ./nanobot-config:/root/.nanobot
+      - ./MCP:/opt/mcp
    ports:
      - "18790:18790"
+      - "8005:8005"

  nanobot-cli:
    build:
@@ -22,3 +24,4 @@ services:
    tty: true
    volumes:
      - ./nanobot-config:/root/.nanobot
+      - ./MCP:/opt/mcp
--- a/nanobot-config/config.json
+++ b/nanobot-config/config.json
@@ -4,9 +4,9 @@
      "workspace": "~/.nanobot/workspace",
      "model": "glm-4.7",
      "provider": "zhipu",
-      "maxTokens": 8192,
+      "maxTokens": 4096,
      "temperature": 0.1,
-      "maxToolIterations": 20,
+      "maxToolIterations": 10,
      "memoryWindow": 100,
      "reasoningEffort": null
    }
@@ -282,13 +282,9 @@
      },
      "mars-datacube": {
        "type": null,
-        "command": "uv",
+        "command": "python",
        "args": [
-          "run",
-          "--directory",
-          "/home/la-mars/studio/mars-mcp",
-          "python",
-          "server.py"
+          "/opt/mcp/mars-mcp/server.py"
        ],
        "env": {},
        "url": "",
@@ -297,13 +293,9 @@
      },
      "map-mcp": {
        "type": null,
-        "command": "uv",
+        "command": "python",
        "args": [
-          "run",
-          "--directory",
-          "/home/la-mars/studio/map-mcp",
-          "python",
-          "server.py"
+          "/opt/mcp/map-mcp/server.py"
        ],
        "env": {},
        "url": "",
--- a/nanobot-config/workspace/memory/MEMORY.md
+++ b/nanobot-config/workspace/memory/MEMORY.md
@@ -22,6 +22,7 @@ This file stores important information that should persist across sessions.
 - Prefers detailed geological analysis and feature descriptions
 - Interested in scientific value and geological evolution of Martian features
 - Natural language interaction style for all operations
+- **When web search is needed, prioritize using multi-search-engine skill** (17 engines including Baidu, Google, DuckDuckGo, WolframAlpha, etc.)

 ## Project Context

--- a/nanobot-config/workspace/skills/multi-search-engine-2-0-1/.clawhub/origin.json
+++ b/nanobot-config/workspace/skills/multi-search-engine-2-0-1/.clawhub/origin.json
@@ -0,0 +1,7 @@
+{
+  "version": 1,
+  "registry": "https://clawhub.ai",
+  "slug": "multi-search-engine-2-0-1",
+  "installedVersion": "1.0.0",
+  "installedAt": 1773487202552
+}
--- a/nanobot-config/workspace/skills/multi-search-engine-2-0-1/CHANGELOG.md
+++ b/nanobot-config/workspace/skills/multi-search-engine-2-0-1/CHANGELOG.md
@@ -0,0 +1,15 @@
+# Changelog
+
+## v2.0.1 (2026-02-06)
+- Simplified documentation
+- Removed gov-related content
+- Optimized for ClawHub publishing
+
+## v2.0.0 (2026-02-06)
+- Added 9 international search engines
+- Enhanced advanced search capabilities
+- Added DuckDuckGo Bangs support
+- Added WolframAlpha knowledge queries
+
+## v1.0.0 (2026-02-04)
+- Initial release with 8 domestic search engines
--- a/nanobot-config/workspace/skills/multi-search-engine-2-0-1/CHANNELLOG.md
+++ b/nanobot-config/workspace/skills/multi-search-engine-2-0-1/CHANNELLOG.md
@@ -0,0 +1,48 @@
+# Multi Search Engine
+
+## 基本信息
+
+- **名称**: multi-search-engine
+- **版本**: v2.0.1
+- **描述**: 集成17个搜索引擎（8国内+9国际），支持高级搜索语法
+- **发布时间**: 2026-02-06
+
+## 搜索引擎
+
+**国内（8个）**: 百度、必应、360、搜狗、微信、头条、集思录
+**国际（9个）**: Google、DuckDuckGo、Yahoo、Brave、Startpage、Ecosia、Qwant、WolframAlpha
+
+## 核心功能
+
+- 高级搜索操作符（site:, filetype:, intitle:等）
+- DuckDuckGo Bangs快捷命令
+- 时间筛选（小时/天/周/月/年）
+- 隐私保护搜索
+- WolframAlpha知识计算
+
+## 更新记录
+
+### v2.0.1 (2026-02-06)
+- 精简文档，优化发布
+
+### v2.0.0 (2026-02-06)
+- 新增9个国际搜索引擎
+- 强化深度搜索能力
+
+### v1.0.0 (2026-02-04)
+- 初始版本：8个国内搜索引擎
+
+## 使用示例
+
+```javascript
+// Google搜索
+web_fetch({"url": "https://www.google.com/search?q=python"})
+
+// 隐私搜索
+web_fetch({"url": "https://duckduckgo.com/html/?q=privacy"})
+
+// 站内搜索
+web_fetch({"url": "https://www.google.com/search?q=site:github.com+python"})
+```
+
+MIT License
--- a/nanobot-config/workspace/skills/multi-search-engine-2-0-1/SKILL.md
+++ b/nanobot-config/workspace/skills/multi-search-engine-2-0-1/SKILL.md
@@ -0,0 +1,110 @@
+---
+name: "multi-search-engine"
+description: "Multi search engine integration with 17 engines (8 CN + 9 Global). Supports advanced search operators, time filters, site search, privacy engines, and WolframAlpha knowledge queries. No API keys required."
+---
+
+# Multi Search Engine v2.0.1
+
+Integration of 17 search engines for web crawling without API keys.
+
+## Search Engines
+
+### Domestic (8)
+- **Baidu**: `https://www.baidu.com/s?wd={keyword}`
+- **Bing CN**: `https://cn.bing.com/search?q={keyword}&ensearch=0`
+- **Bing INT**: `https://cn.bing.com/search?q={keyword}&ensearch=1`
+- **360**: `https://www.so.com/s?q={keyword}`
+- **Sogou**: `https://sogou.com/web?query={keyword}`
+- **WeChat**: `https://wx.sogou.com/weixin?type=2&query={keyword}`
+- **Toutiao**: `https://so.toutiao.com/search?keyword={keyword}`
+- **Jisilu**: `https://www.jisilu.cn/explore/?keyword={keyword}`
+
+### International (9)
+- **Google**: `https://www.google.com/search?q={keyword}`
+- **Google HK**: `https://www.google.com.hk/search?q={keyword}`
+- **DuckDuckGo**: `https://duckduckgo.com/html/?q={keyword}`
+- **Yahoo**: `https://search.yahoo.com/search?p={keyword}`
+- **Startpage**: `https://www.startpage.com/sp/search?query={keyword}`
+- **Brave**: `https://search.brave.com/search?q={keyword}`
+- **Ecosia**: `https://www.ecosia.org/search?q={keyword}`
+- **Qwant**: `https://www.qwant.com/?q={keyword}`
+- **WolframAlpha**: `https://www.wolframalpha.com/input?i={keyword}`
+
+## Quick Examples
+
+```javascript
+// Basic search
+web_fetch({"url": "https://www.google.com/search?q=python+tutorial"})
+
+// Site-specific
+web_fetch({"url": "https://www.google.com/search?q=site:github.com+react"})
+
+// File type
+web_fetch({"url": "https://www.google.com/search?q=machine+learning+filetype:pdf"})
+
+// Time filter (past week)
+web_fetch({"url": "https://www.google.com/search?q=ai+news&tbs=qdr:w"})
+
+// Privacy search
+web_fetch({"url": "https://duckduckgo.com/html/?q=privacy+tools"})
+
+// DuckDuckGo Bangs
+web_fetch({"url": "https://duckduckgo.com/html/?q=!gh+tensorflow"})
+
+// Knowledge calculation
+web_fetch({"url": "https://www.wolframalpha.com/input?i=100+USD+to+CNY"})
+```
+
+## Advanced Operators
+
+| Operator | Example | Description |
+|----------|---------|-------------|
+| `site:` | `site:github.com python` | Search within site |
+| `filetype:` | `filetype:pdf report` | Specific file type |
+| `""` | `"machine learning"` | Exact match |
+| `-` | `python -snake` | Exclude term |
+| `OR` | `cat OR dog` | Either term |
+
+## Time Filters
+
+| Parameter | Description |
+|-----------|-------------|
+| `tbs=qdr:h` | Past hour |
+| `tbs=qdr:d` | Past day |
+| `tbs=qdr:w` | Past week |
+| `tbs=qdr:m` | Past month |
+| `tbs=qdr:y` | Past year |
+
+## Privacy Engines
+
+- **DuckDuckGo**: No tracking
+- **Startpage**: Google results + privacy
+- **Brave**: Independent index
+- **Qwant**: EU GDPR compliant
+
+## Bangs Shortcuts (DuckDuckGo)
+
+| Bang | Destination |
+|------|-------------|
+| `!g` | Google |
+| `!gh` | GitHub |
+| `!so` | Stack Overflow |
+| `!w` | Wikipedia |
+| `!yt` | YouTube |
+
+## WolframAlpha Queries
+
+- Math: `integrate x^2 dx`
+- Conversion: `100 USD to CNY`
+- Stocks: `AAPL stock`
+- Weather: `weather in Beijing`
+
+## Documentation
+
+- `references/advanced-search.md` - Domestic search guide
+- `references/international-search.md` - International search guide
+- `CHANGELOG.md` - Version history
+
+## License
+
+MIT
--- a/nanobot-config/workspace/skills/multi-search-engine-2-0-1/_meta.json
+++ b/nanobot-config/workspace/skills/multi-search-engine-2-0-1/_meta.json
@@ -0,0 +1,6 @@
+{
+  "ownerId": "kn7d2tpj79y18y1rrd8g4fx08d8267f2",
+  "slug": "multi-search-engine-2-0-1",
+  "version": "1.0.0",
+  "publishedAt": 1772543153534
+}
--- a/nanobot-config/workspace/skills/multi-search-engine-2-0-1/config.json
+++ b/nanobot-config/workspace/skills/multi-search-engine-2-0-1/config.json
@@ -0,0 +1,22 @@
+{
+  "name": "multi-search-engine",
+  "engines": [
+    {"name": "Baidu", "url": "https://www.baidu.com/s?wd={keyword}", "region": "cn"},
+    {"name": "Bing CN", "url": "https://cn.bing.com/search?q={keyword}&ensearch=0", "region": "cn"},
+    {"name": "Bing INT", "url": "https://cn.bing.com/search?q={keyword}&ensearch=1", "region": "cn"},
+    {"name": "360", "url": "https://www.so.com/s?q={keyword}", "region": "cn"},
+    {"name": "Sogou", "url": "https://sogou.com/web?query={keyword}", "region": "cn"},
+    {"name": "WeChat", "url": "https://wx.sogou.com/weixin?type=2&query={keyword}", "region": "cn"},
+    {"name": "Toutiao", "url": "https://so.toutiao.com/search?keyword={keyword}", "region": "cn"},
+    {"name": "Jisilu", "url": "https://www.jisilu.cn/explore/?keyword={keyword}", "region": "cn"},
+    {"name": "Google", "url": "https://www.google.com/search?q={keyword}", "region": "global"},
+    {"name": "Google HK", "url": "https://www.google.com.hk/search?q={keyword}", "region": "global"},
+    {"name": "DuckDuckGo", "url": "https://duckduckgo.com/html/?q={keyword}", "region": "global"},
+    {"name": "Yahoo", "url": "https://search.yahoo.com/search?p={keyword}", "region": "global"},
+    {"name": "Startpage", "url": "https://www.startpage.com/sp/search?query={keyword}", "region": "global"},
+    {"name": "Brave", "url": "https://search.brave.com/search?q={keyword}", "region": "global"},
+    {"name": "Ecosia", "url": "https://www.ecosia.org/search?q={keyword}", "region": "global"},
+    {"name": "Qwant", "url": "https://www.qwant.com/?q={keyword}", "region": "global"},
+    {"name": "WolframAlpha", "url": "https://www.wolframalpha.com/input?i={keyword}", "region": "global"}
+  ]
+}
--- a/nanobot-config/workspace/skills/multi-search-engine-2-0-1/metadata.json
+++ b/nanobot-config/workspace/skills/multi-search-engine-2-0-1/metadata.json
@@ -0,0 +1,7 @@
+{
+  "name": "multi-search-engine",
+  "version": "2.0.1",
+  "description": "Multi search engine with 17 engines (8 CN + 9 Global). Supports advanced operators, time filters, privacy engines.",
+  "engines": 17,
+  "requires_api_key": false
+}
--- a/nanobot-config/workspace/skills/multi-search-engine-2-0-1/references/international-search.md
+++ b/nanobot-config/workspace/skills/multi-search-engine-2-0-1/references/international-search.md
@@ -0,0 +1,651 @@
+# 国际搜索引擎深度搜索指南
+
+## 🔍 Google 深度搜索
+
+### 1.1 基础高级搜索操作符
+
+| 操作符 | 功能 | 示例 | URL |
+|--------|------|------|-----|
+| `""` | 精确匹配 | `"machine learning"` | `https://www.google.com/search?q=%22machine+learning%22` |
+| `-` | 排除关键词 | `python -snake` | `https://www.google.com/search?q=python+-snake` |
+| `OR` | 或运算 | `machine learning OR deep learning` | `https://www.google.com/search?q=machine+learning+OR+deep+learning` |
+| `*` | 通配符 | `machine * algorithms` | `https://www.google.com/search?q=machine+*+algorithms` |
+| `()` | 分组 | `(apple OR microsoft) phones` | `https://www.google.com/search?q=(apple+OR+microsoft)+phones` |
+| `..` | 数字范围 | `laptop $500..$1000` | `https://www.google.com/search?q=laptop+%24500..%241000` |
+
+### 1.2 站点与文件搜索
+
+| 操作符 | 功能 | 示例 |
+|--------|------|------|
+| `site:` | 站内搜索 | `site:github.com python projects` |
+| `filetype:` | 文件类型 | `filetype:pdf annual report` |
+| `inurl:` | URL包含 | `inurl:login admin` |
+| `intitle:` | 标题包含 | `intitle:"index of" mp3` |
+| `intext:` | 正文包含 | `intext:password filetype:txt` |
+| `cache:` | 查看缓存 | `cache:example.com` |
+| `related:` | 相关网站 | `related:github.com` |
+| `info:` | 网站信息 | `info:example.com` |
+
+### 1.3 时间筛选参数
+
+| 参数 | 含义 | URL示例 |
+|------|------|---------|
+| `tbs=qdr:h` | 过去1小时 | `https://www.google.com/search?q=news&tbs=qdr:h` |
+| `tbs=qdr:d` | 过去24小时 | `https://www.google.com/search?q=news&tbs=qdr:d` |
+| `tbs=qdr:w` | 过去1周 | `https://www.google.com/search?q=news&tbs=qdr:w` |
+| `tbs=qdr:m` | 过去1月 | `https://www.google.com/search?q=news&tbs=qdr:m` |
+| `tbs=qdr:y` | 过去1年 | `https://www.google.com/search?q=news&tbs=qdr:y` |
+| `tbs=cdr:1,cd_min:1/1/2024,cd_max:12/31/2024` | 自定义日期范围 | 2024年全年 |
+
+### 1.4 语言和地区筛选
+
+| 参数 | 功能 | 示例 |
+|------|------|------|
+| `hl=en` | 界面语言 | `https://www.google.com/search?q=test&hl=en` |
+| `lr=lang_zh-CN` | 搜索结果语言 | `https://www.google.com/search?q=test&lr=lang_zh-CN` |
+| `cr=countryCN` | 国家/地区 | `https://www.google.com/search?q=test&cr=countryCN` |
+| `gl=us` | 地理位置 | `https://www.google.com/search?q=test&gl=us` |
+
+### 1.5 特殊搜索类型
+
+| 类型 | URL | 说明 |
+|------|-----|------|
+| 图片搜索 | `https://www.google.com/search?q={keyword}&tbm=isch` | `tbm=isch` 表示图片 |
+| 新闻搜索 | `https://www.google.com/search?q={keyword}&tbm=nws` | `tbm=nws` 表示新闻 |
+| 视频搜索 | `https://www.google.com/search?q={keyword}&tbm=vid` | `tbm=vid` 表示视频 |
+| 地图搜索 | `https://www.google.com/search?q={keyword}&tbm=map` | `tbm=map` 表示地图 |
+| 购物搜索 | `https://www.google.com/search?q={keyword}&tbm=shop` | `tbm=shop` 表示购物 |
+| 图书搜索 | `https://www.google.com/search?q={keyword}&tbm=bks` | `tbm=bks` 表示图书 |
+| 学术搜索 | `https://scholar.google.com/scholar?q={keyword}` | Google Scholar |
+
+### 1.6 Google 深度搜索示例
+
+```javascript
+// 1. 搜索GitHub上的Python机器学习项目
+web_fetch({"url": "https://www.google.com/search?q=site:github.com+python+machine+learning"})
+
+// 2. 搜索2024年的PDF格式机器学习教程
+web_fetch({"url": "https://www.google.com/search?q=machine+learning+tutorial+filetype:pdf&tbs=cdr:1,cd_min:1/1/2024"})
+
+// 3. 搜索标题包含"tutorial"的Python相关页面
+web_fetch({"url": "https://www.google.com/search?q=intitle:tutorial+python"})
+
+// 4. 搜索过去一周的新闻
+web_fetch({"url": "https://www.google.com/search?q=AI+breakthrough&tbs=qdr:w&tbm=nws"})
+
+// 5. 搜索中文内容（界面英文，结果中文）
+web_fetch({"url": "https://www.google.com/search?q=人工智能&lr=lang_zh-CN&hl=en"})
+
+// 6. 搜索特定价格范围的笔记本电脑
+web_fetch({"url": "https://www.google.com/search?q=laptop+%241000..%242000+best+rating"})
+
+// 7. 搜索排除Wikipedia的结果
+web_fetch({"url": "https://www.google.com/search?q=python+programming+-wikipedia"})
+
+// 8. 搜索学术文献
+web_fetch({"url": "https://scholar.google.com/scholar?q=deep+learning+optimization"})
+
+// 9. 搜索缓存页面（查看已删除内容）
+web_fetch({"url": "https://webcache.googleusercontent.com/search?q=cache:example.com"})
+
+// 10. 搜索相关网站
+web_fetch({"url": "https://www.google.com/search?q=related:stackoverflow.com"})
+```
+
+---
+
+## 🦆 DuckDuckGo 深度搜索
+
+### 2.1 DuckDuckGo 特色功能
+
+| 功能 | 语法 | 示例 |
+|------|------|------|
+| **Bangs 快捷** | `!缩写` | `!g python` → Google搜索 |
+| **密码生成** | `password` | `https://duckduckgo.com/?q=password+20` |
+| **颜色转换** | `color` | `https://duckduckgo.com/?q=+%23FF5733` |
+| **短链接** | `shorten` | `https://duckduckgo.com/?q=shorten+example.com` |
+| **二维码生成** | `qr` | `https://duckduckgo.com/?q=qr+hello+world` |
+| **生成UUID** | `uuid` | `https://duckduckgo.com/?q=uuid` |
+| **Base64编解码** | `base64` | `https://duckduckgo.com/?q=base64+hello` |
+
+### 2.2 DuckDuckGo Bangs 完整列表
+
+#### 搜索引擎
+
+| Bang | 跳转目标 | 示例 |
+|------|---------|------|
+| `!g` | Google | `!g python tutorial` |
+| `!b` | Bing | `!b weather` |
+| `!y` | Yahoo | `!y finance` |
+| `!sp` | Startpage | `!sp privacy` |
+| `!brave` | Brave Search | `!brave tech` |
+
+#### 编程开发
+
+| Bang | 跳转目标 | 示例 |
+|------|---------|------|
+| `!gh` | GitHub | `!gh tensorflow` |
+| `!so` | Stack Overflow | `!so javascript error` |
+| `!npm` | npmjs.com | `!npm express` |
+| `!pypi` | PyPI | `!pypi requests` |
+| `!mdn` | MDN Web Docs | `!mdn fetch api` |
+| `!docs` | DevDocs | `!docs python` |
+| `!docker` | Docker Hub | `!docker nginx` |
+
+#### 知识百科
+
+| Bang | 跳转目标 | 示例 |
+|------|---------|------|
+| `!w` | Wikipedia | `!w machine learning` |
+| `!wen` | Wikipedia英文 | `!wen artificial intelligence` |
+| `!wt` | Wiktionary | `!wt serendipity` |
+| `!imdb` | IMDb | `!imdb inception` |
+
+#### 购物价格
+
+| Bang | 跳转目标 | 示例 |
+|------|---------|------|
+| `!a` | Amazon | `!a wireless headphones` |
+| `!e` | eBay | `!e vintage watch` |
+| `!ali` | AliExpress | `!ali phone case` |
+
+#### 地图位置
+
+| Bang | 跳转目标 | 示例 |
+|------|---------|------|
+| `!m` | Google Maps | `!m Beijing` |
+| `!maps` | OpenStreetMap | `!maps Paris` |
+
+### 2.3 DuckDuckGo 搜索参数
+
+| 参数 | 功能 | 示例 |
+|------|------|------|
+| `kp=1` | 严格安全搜索 | `https://duckduckgo.com/html/?q=test&kp=1` |
+| `kp=-1` | 关闭安全搜索 | `https://duckduckgo.com/html/?q=test&kp=-1` |
+| `kl=cn` | 中国区域 | `https://duckduckgo.com/html/?q=news&kl=cn` |
+| `kl=us-en` | 美国英文 | `https://duckduckgo.com/html/?q=news&kl=us-en` |
+| `ia=web` | 网页结果 | `https://duckduckgo.com/?q=test&ia=web` |
+| `ia=images` | 图片结果 | `https://duckduckgo.com/?q=test&ia=images` |
+| `ia=news` | 新闻结果 | `https://duckduckgo.com/?q=test&ia=news` |
+| `ia=videos` | 视频结果 | `https://duckduckgo.com/?q=test&ia=videos` |
+
+### 2.4 DuckDuckGo 深度搜索示例
+
+```javascript
+// 1. 使用Bang跳转到Google搜索
+web_fetch({"url": "https://duckduckgo.com/html/?q=!g+machine+learning"})
+
+// 2. 直接搜索GitHub上的项目
+web_fetch({"url": "https://duckduckgo.com/html/?q=!gh+react"})
+
+// 3. 查找Stack Overflow答案
+web_fetch({"url": "https://duckduckgo.com/html/?q=!so+python+list+comprehension"})
+
+// 4. 生成密码
+web_fetch({"url": "https://duckduckgo.com/?q=password+16"})
+
+// 5. Base64编码
+web_fetch({"url": "https://duckduckgo.com/?q=base64+hello+world"})
+
+// 6. 颜色代码转换
+web_fetch({"url": "https://duckduckgo.com/?q=%23FF5733"})
+
+// 7. 搜索YouTube视频
+web_fetch({"url": "https://duckduckgo.com/html/?q=!yt+python+tutorial"})
+
+// 8. 查看Wikipedia
+web_fetch({"url": "https://duckduckgo.com/html/?q=!w+artificial+intelligence"})
+
+// 9. 亚马逊商品搜索
+web_fetch({"url": "https://duckduckgo.com/html/?q=!a+laptop"})
+
+// 10. 生成二维码
+web_fetch({"url": "https://duckduckgo.com/?q=qr+https://github.com"})
+```
+
+---
+
+## 🔎 Brave Search 深度搜索
+
+### 3.1 Brave Search 特色功能
+
+| 功能 | 参数 | 示例 |
+|------|------|------|
+| **独立索引** | 无依赖Google/Bing | 自有爬虫索引 |
+| **Goggles** | 自定义搜索规则 | 创建个性化过滤器 |
+| **Discussions** | 论坛讨论搜索 | 聚合Reddit等论坛 |
+| **News** | 新闻聚合 | 独立新闻索引 |
+
+### 3.2 Brave Search 参数
+
+| 参数 | 功能 | 示例 |
+|------|------|------|
+| `tf=pw` | 本周 | `https://search.brave.com/search?q=news&tf=pw` |
+| `tf=pm` | 本月 | `https://search.brave.com/search?q=tech&tf=pm` |
+| `tf=py` | 本年 | `https://search.brave.com/search?q=AI&tf=py` |
+| `safesearch=strict` | 严格安全 | `https://search.brave.com/search?q=test&safesearch=strict` |
+| `source=web` | 网页搜索 | 默认 |
+| `source=news` | 新闻搜索 | `https://search.brave.com/search?q=tech&source=news` |
+| `source=images` | 图片搜索 | `https://search.brave.com/search?q=cat&source=images` |
+| `source=videos` | 视频搜索 | `https://search.brave.com/search?q=music&source=videos` |
+
+### 3.3 Brave Search Goggles（自定义过滤器）
+
+Goggles 允许创建自定义搜索规则：
+
+```
+$discard  // 丢弃所有
+$boost,site=stackoverflow.com  // 提升Stack Overflow
+$boost,site=github.com  // 提升GitHub
+$boost,site=docs.python.org  // 提升Python文档
+```
+
+### 3.4 Brave Search 深度搜索示例
+
+```javascript
+// 1. 本周科技新闻
+web_fetch({"url": "https://search.brave.com/search?q=technology&tf=pw&source=news"})
+
+// 2. 本月AI发展
+web_fetch({"url": "https://search.brave.com/search?q=artificial+intelligence&tf=pm"})
+
+// 3. 图片搜索
+web_fetch({"url": "https://search.brave.com/search?q=machine+learning&source=images"})
+
+// 4. 视频教程
+web_fetch({"url": "https://search.brave.com/search?q=python+tutorial&source=videos"})
+
+// 5. 使用独立索引搜索
+web_fetch({"url": "https://search.brave.com/search?q=privacy+tools"})
+```
+
+---
+
+## 📊 WolframAlpha 知识计算搜索
+
+### 4.1 WolframAlpha 数据类型
+
+| 类型 | 查询示例 | URL |
+|------|---------|-----|
+| **数学计算** | `integrate x^2 dx` | `https://www.wolframalpha.com/input?i=integrate+x%5E2+dx` |
+| **单位换算** | `100 miles to km` | `https://www.wolframalpha.com/input?i=100+miles+to+km` |
+| **货币转换** | `100 USD to CNY` | `https://www.wolframalpha.com/input?i=100+USD+to+CNY` |
+| **股票数据** | `AAPL stock` | `https://www.wolframalpha.com/input?i=AAPL+stock` |
+| **天气查询** | `weather in Beijing` | `https://www.wolframalpha.com/input?i=weather+in+Beijing` |
+| **人口数据** | `population of China` | `https://www.wolframalpha.com/input?i=population+of+China` |
+| **化学元素** | `properties of gold` | `https://www.wolframalpha.com/input?i=properties+of+gold` |
+| **营养成分** | `nutrition of apple` | `https://www.wolframalpha.com/input?i=nutrition+of+apple` |
+| **日期计算** | `days between Jan 1 2020 and Dec 31 2024` | 日期间隔计算 |
+| **时区转换** | `10am Beijing to New York` | 时区转换 |
+| **IP地址** | `8.8.8.8` | IP信息查询 |
+| **条形码** | `scan barcode 123456789` | 条码信息 |
+| **飞机航班** | `flight AA123` | 航班信息 |
+
+### 4.2 WolframAlpha 深度搜索示例
+
+```javascript
+// 1. 计算积分
+web_fetch({"url": "https://www.wolframalpha.com/input?i=integrate+sin%28x%29+from+0+to+pi"})
+
+// 2. 解方程
+web_fetch({"url": "https://www.wolframalpha.com/input?i=solve+x%5E2-5x%2B6%3D0"})
+
+// 3. 货币实时汇率
+web_fetch({"url": "https://www.wolframalpha.com/input?i=100+USD+to+CNY"})
+
+// 4. 股票实时数据
+web_fetch({"url": "https://www.wolframalpha.com/input?i=Apple+stock+price"})
+
+// 5. 城市天气
+web_fetch({"url": "https://www.wolframalpha.com/input?i=weather+in+Shanghai+tomorrow"})
+
+// 6. 国家统计信息
+web_fetch({"url": "https://www.wolframalpha.com/input?i=GDP+of+China+vs+USA"})
+
+// 7. 化学计算
+web_fetch({"url": "https://www.wolframalpha.com/input?i=molar+mass+of+H2SO4"})
+
+// 8. 物理常数
+web_fetch({"url": "https://www.wolframalpha.com/input?i=speed+of+light"})
+
+// 9. 营养信息
+web_fetch({"url": "https://www.wolframalpha.com/input?i=calories+in+banana"})
+
+// 10. 历史日期
+web_fetch({"url": "https://www.wolframalpha.com/input?i=events+on+July+20+1969"})
+```
+
+---
+
+## 🔧 Startpage 隐私搜索
+
+### 5.1 Startpage 特色功能
+
+| 功能 | 说明 | URL |
+|------|------|-----|
+| **代理浏览** | 匿名访问搜索结果 | 点击"匿名查看" |
+| **无追踪** | 不记录搜索历史 | 默认开启 |
+| **EU服务器** | 受欧盟隐私法保护 | 数据在欧洲 |
+| **代理图片** | 图片代理加载 | 隐藏IP |
+
+### 5.2 Startpage 参数
+
+| 参数 | 功能 | 示例 |
+|------|------|------|
+| `cat=web` | 网页搜索 | 默认 |
+| `cat=images` | 图片搜索 | `...&cat=images` |
+| `cat=video` | 视频搜索 | `...&cat=video` |
+| `cat=news` | 新闻搜索 | `...&cat=news` |
+| `language=english` | 英文结果 | `...&language=english` |
+| `time=day` | 过去24小时 | `...&time=day` |
+| `time=week` | 过去一周 | `...&time=week` |
+| `time=month` | 过去一月 | `...&time=month` |
+| `time=year` | 过去一年 | `...&time=year` |
+| `nj=0` | 关闭 family filter | `...&nj=0` |
+
+### 5.3 Startpage 深度搜索示例
+
+```javascript
+// 1. 隐私搜索
+web_fetch({"url": "https://www.startpage.com/sp/search?query=privacy+tools"})
+
+// 2. 图片隐私搜索
+web_fetch({"url": "https://www.startpage.com/sp/search?query=nature&cat=images"})
+
+// 3. 本周新闻（隐私模式）
+web_fetch({"url": "https://www.startpage.com/sp/search?query=tech+news&time=week&cat=news"})
+
+// 4. 英文结果搜索
+web_fetch({"url": "https://www.startpage.com/sp/search?query=machine+learning&language=english"})
+```
+
+---
+
+## 🌍 综合搜索策略
+
+### 6.1 按搜索目标选择引擎
+
+| 搜索目标 | 首选引擎 | 备选引擎 | 原因 |
+|---------|---------|---------|------|
+| **学术研究** | Google Scholar | Google, Brave | 学术资源索引 |
+| **编程开发** | Google | GitHub(DuckDuckGo bang) | 技术文档全面 |
+| **隐私敏感** | DuckDuckGo | Startpage, Brave | 不追踪用户 |
+| **实时新闻** | Brave News | Google News | 独立新闻索引 |
+| **知识计算** | WolframAlpha | Google | 结构化数据 |
+| **中文内容** | Google HK | Bing | 中文优化好 |
+| **欧洲视角** | Qwant | Startpage | 欧盟合规 |
+| **环保支持** | Ecosia | DuckDuckGo | 搜索植树 |
+| **无过滤** | Brave | Startpage | 无偏见结果 |
+
+### 6.2 多引擎交叉验证
+
+```javascript
+// 策略：同一关键词多引擎搜索，对比结果
+const keyword = "climate change 2024";
+
+// 获取不同视角
+const searches = [
+  { engine: "Google", url: `https://www.google.com/search?q=${keyword}&tbs=qdr:m` },
+  { engine: "Brave", url: `https://search.brave.com/search?q=${keyword}&tf=pm` },
+  { engine: "DuckDuckGo", url: `https://duckduckgo.com/html/?q=${keyword}` },
+  { engine: "Ecosia", url: `https://www.ecosia.org/search?q=${keyword}` }
+];
+
+// 分析不同引擎的结果差异
+```
+
+### 6.3 时间敏感搜索策略
+
+| 时效性要求 | 引擎选择 | 参数设置 |
+|-----------|---------|---------|
+| **实时（小时级）** | Google News, Brave News | `tbs=qdr:h`, `tf=pw` |
+| **近期（天级）** | Google, Brave | `tbs=qdr:d`, `time=day` |
+| **本周** | 所有引擎 | `tbs=qdr:w`, `tf=pw` |
+| **本月** | 所有引擎 | `tbs=qdr:m`, `tf=pm` |
+| **历史** | Google Scholar | 学术档案 |
+
+### 6.4 专业领域深度搜索
+
+#### 技术开发
+
+```javascript
+// GitHub 项目搜索
+web_fetch({"url": "https://duckduckgo.com/html/?q=!gh+tensorflow+stars:%3E1000"})
+
+// Stack Overflow 问题
+web_fetch({"url": "https://duckduckgo.com/html/?q=!so+python+memory+leak"})
+
+// MDN 文档
+web_fetch({"url": "https://duckduckgo.com/html/?q=!mdn+javascript+async+await"})
+
+// PyPI 包
+web_fetch({"url": "https://duckduckgo.com/html/?q=!pypi+requests"})
+
+// npm 包
+web_fetch({"url": "https://duckduckgo.com/html/?q=!npm+express"})
+```
+
+#### 学术研究
+
+```javascript
+// Google Scholar 论文
+web_fetch({"url": "https://scholar.google.com/scholar?q=deep+learning+2024"})
+
+// 搜索PDF论文
+web_fetch({"url": "https://www.google.com/search?q=machine+learning+filetype:pdf+2024"})
+
+// arXiv 论文
+web_fetch({"url": "https://duckduckgo.com/html/?q=site:arxiv.org+quantum+computing"})
+```
+
+#### 金融投资
+
+```javascript
+// 股票实时数据
+web_fetch({"url": "https://www.wolframalpha.com/input?i=AAPL+stock"})
+
+// 汇率转换
+web_fetch({"url": "https://www.wolframalpha.com/input?i=EUR+to+USD"})
+
+// 搜索财报PDF
+web_fetch({"url": "https://www.google.com/search?q=Apple+Q4+2024+earnings+filetype:pdf"})
+```
+
+#### 新闻时事
+
+```javascript
+// Google新闻
+web_fetch({"url": "https://www.google.com/search?q=breaking+news&tbm=nws&tbs=qdr:h"})
+
+// Brave新闻
+web_fetch({"url": "https://search.brave.com/search?q=world+news&source=news"})
+
+// DuckDuckGo新闻
+web_fetch({"url": "https://duckduckgo.com/html/?q=tech+news&ia=news"})
+```
+
+---
+
+## 🛠️ 高级搜索技巧汇总
+
+### URL编码工具函数
+
+```javascript
+// URL编码关键词
+function encodeKeyword(keyword) {
+  return encodeURIComponent(keyword);
+}
+
+// 示例
+const keyword = "machine learning";
+const encoded = encodeKeyword(keyword); // "machine%20learning"
+```
+
+### 批量搜索模板
+
+```javascript
+// 多引擎批量搜索函数
+function generateSearchUrls(keyword) {
+  const encoded = encodeURIComponent(keyword);
+  return {
+    google: `https://www.google.com/search?q=${encoded}`,
+    google_hk: `https://www.google.com.hk/search?q=${encoded}`,
+    duckduckgo: `https://duckduckgo.com/html/?q=${encoded}`,
+    brave: `https://search.brave.com/search?q=${encoded}`,
+    startpage: `https://www.startpage.com/sp/search?query=${encoded}`,
+    bing_intl: `https://cn.bing.com/search?q=${encoded}&ensearch=1`,
+    yahoo: `https://search.yahoo.com/search?p=${encoded}`,
+    ecosia: `https://www.ecosia.org/search?q=${encoded}`,
+    qwant: `https://www.qwant.com/?q=${encoded}`
+  };
+}
+
+// 使用示例
+const urls = generateSearchUrls("artificial intelligence");
+```
+
+### 时间筛选快捷函数
+
+```javascript
+// Google时间筛选URL生成
+function googleTimeSearch(keyword, period) {
+  const periods = {
+    hour: 'qdr:h',
+    day: 'qdr:d',
+    week: 'qdr:w',
+    month: 'qdr:m',
+    year: 'qdr:y'
+  };
+  return `https://www.google.com/search?q=${encodeURIComponent(keyword)}&tbs=${periods[period]}`;
+}
+
+// 使用示例
+const recentNews = googleTimeSearch("AI breakthrough", "week");
+```
+
+---
+
+## 📝 完整搜索示例集
+
+```javascript
+// ==================== 技术开发 ====================
+
+// 1. 搜索GitHub上高Star的Python项目
+web_fetch({"url": "https://www.google.com/search?q=site:github.com+python+stars:%3E1000"})
+
+// 2. Stack Overflow最佳答案
+web_fetch({"url": "https://duckduckgo.com/html/?q=!so+best+way+to+learn+python"})
+
+// 3. MDN文档查询
+web_fetch({"url": "https://duckduckgo.com/html/?q=!mdn+promises"})
+
+// 4. 搜索npm包
+web_fetch({"url": "https://duckduckgo.com/html/?q=!npm+axios"})
+
+// ==================== 学术研究 ====================
+
+// 5. Google Scholar论文
+web_fetch({"url": "https://scholar.google.com/scholar?q=transformer+architecture"})
+
+// 6. 搜索PDF论文
+web_fetch({"url": "https://www.google.com/search?q=attention+is+all+you+need+filetype:pdf"})
+
+// 7. arXiv最新论文
+web_fetch({"url": "https://duckduckgo.com/html/?q=site:arxiv.org+abs+quantum"})
+
+// ==================== 新闻时事 ====================
+
+// 8. Google最新新闻（过去1小时）
+web_fetch({"url": "https://www.google.com/search?q=breaking+news&tbs=qdr:h&tbm=nws"})
+
+// 9. Brave本周科技新闻
+web_fetch({"url": "https://search.brave.com/search?q=technology&tf=pw&source=news"})
+
+// 10. DuckDuckGo新闻
+web_fetch({"url": "https://duckduckgo.com/html/?q=world+news&ia=news"})
+
+// ==================== 金融投资 ====================
+
+// 11. 股票实时数据
+web_fetch({"url": "https://www.wolframalpha.com/input?i=Tesla+stock"})
+
+// 12. 货币汇率
+web_fetch({"url": "https://www.wolframalpha.com/input?i=1+BTC+to+USD"})
+
+// 13. 公司财报PDF
+web_fetch({"url": "https://www.google.com/search?q=Microsoft+annual+report+2024+filetype:pdf"})
+
+// ==================== 知识计算 ====================
+
+// 14. 数学计算
+web_fetch({"url": "https://www.wolframalpha.com/input?i=derivative+of+x%5E3+sin%28x%29"})
+
+// 15. 单位换算
+web_fetch({"url": "https://www.wolframalpha.com/input?i=convert+100+miles+to+kilometers"})
+
+// 16. 营养信息
+web_fetch({"url": "https://www.wolframalpha.com/input?i=protein+in+chicken+breast"})
+
+// ==================== 隐私保护搜索 ====================
+
+// 17. DuckDuckGo隐私搜索
+web_fetch({"url": "https://duckduckgo.com/html/?q=privacy+tools"})
+
+// 18. Startpage匿名搜索
+web_fetch({"url": "https://www.startpage.com/sp/search?query=secure+messaging"})
+
+// 19. Brave无追踪搜索
+web_fetch({"url": "https://search.brave.com/search?q=encryption+software"})
+
+// ==================== 高级组合搜索 ====================
+
+// 20. Google多条件精确搜索
+web_fetch({"url": "https://www.google.com/search?q=%22machine+learning%22+site:github.com+filetype:pdf+2024"})
+
+// 21. 排除特定站点的搜索
+web_fetch({"url": "https://www.google.com/search?q=python+tutorial+-wikipedia+-w3schools"})
+
+// 22. 价格范围搜索
+web_fetch({"url": "https://www.google.com/search?q=laptop+%24800..%241200+best+review"})
+
+// 23. 使用Bangs快速跳转
+web_fetch({"url": "https://duckduckgo.com/html/?q=!g+site:medium.com+python"})
+
+// 24. 图片搜索（Google）
+web_fetch({"url": "https://www.google.com/search?q=beautiful+landscape&tbm=isch"})
+
+// 25. 学术引用搜索
+web_fetch({"url": "https://scholar.google.com/scholar?q=author:%22Geoffrey+Hinton%22"})
+```
+
+---
+
+## 🔐 隐私保护最佳实践
+
+### 搜索引擎隐私级别
+
+| 引擎 | 追踪级别 | 数据保留 | 加密 | 推荐场景 |
+|------|---------|---------|------|---------|
+| **DuckDuckGo** | 无追踪 | 无保留 | 是 | 日常隐私搜索 |
+| **Startpage** | 无追踪 | 无保留 | 是 | 需要Google结果但保护隐私 |
+| **Brave** | 无追踪 | 无保留 | 是 | 独立索引，无偏见 |
+| **Qwant** | 无追踪 | 无保留 | 是 | 欧盟合规要求 |
+| **Google** | 高度追踪 | 长期保留 | 是 | 需要个性化结果 |
+| **Bing** | 中度追踪 | 长期保留 | 是 | 微软服务集成 |
+
+### 隐私搜索建议
+
+1. **日常使用**: DuckDuckGo 或 Brave
+2. **需要Google结果但保护隐私**: Startpage
+3. **学术研究**: Google Scholar（学术用途追踪较少）
+4. **敏感查询**: 使用Tor浏览器 + DuckDuckGo onion服务
+5. **跨设备同步**: 避免登录搜索引擎账户
+
+---
+
+## 📚 参考资料
+
+- [Google搜索操作符完整列表](https://support.google.com/websearch/answer/...)
+- [DuckDuckGo Bangs完整列表](https://duckduckgo.com/bang)
+- [Brave Search文档](https://search.brave.com/help/...)
+- [WolframAlpha示例](https://www.wolframalpha.com/examples/)