websearch-mcp
Enables web searching via SearXNG, page content extraction with Crawl4AI, and image analysis using vision language models. It provides AI agents with tools for information synthesis and web-based data retrieval through OpenAI-compatible LLM endpoints.
README
websearch-mcp
An MCP server that provides web search and page fetching tools for AI agents. Uses SearXNG for search, Crawl4AI for content extraction, and any OpenAI-compatible LLM for server-side synthesis.
Prerequisites
- Python 3.12+
- SearXNG instance with JSON format enabled (
search.formats: [json]insettings.yml) - OpenAI-compatible LLM endpoint (OpenAI, Ollama, vLLM, LiteLLM, etc.)
Installation
# Run directly from GitHub
uvx --from "git+https://github.com/<org>/websearch-mcp" websearch-mcp
# Or clone and install locally
git clone https://github.com/<org>/websearch-mcp
cd websearch-mcp
uv sync
uv run websearch-mcp
Tools
web_search
Search the web via SearXNG, fetch top result pages, and synthesize with LLM.
| Parameter | Type | Required | Description |
|---|---|---|---|
query |
string | Yes | Search query |
max_results |
int | No | Max results (default: 10) |
allowed_domains |
string[] | No | Only include these domains |
blocked_domains |
string[] | No | Exclude these domains |
webfetch
Fetch a single URL, extract content, and process with LLM.
| Parameter | Type | Required | Description |
|---|---|---|---|
url |
string | Yes | URL to fetch |
prompt |
string | No | Custom instruction for LLM processing |
image-description
Describe an image using a vision language model (VLM). Accepts either base64-encoded image data or an absolute filesystem path to an image file.
| Parameter | Type | Required | Description |
|---|---|---|---|
image |
string | Yes | Base64-encoded image data or absolute filesystem path |
Returns a JSON object with description, success status, and optional error message.
Environment Variables
| Variable | Required | Default | Description |
|---|---|---|---|
SEARXNG_URL |
Yes | — | Base URL of SearXNG instance |
LLM_BASE_URL |
Yes | — | OpenAI-compatible endpoint base URL |
LLM_API_KEY |
Yes | — | API key for the LLM endpoint |
LLM_MODEL |
Yes | — | Model name for chat completions |
CACHE_TTL_SECONDS |
No | 900 |
Cache TTL in seconds (0 to disable) |
CACHE_MAX_ENTRIES |
No | 1000 |
Max cache entries before LRU eviction |
FETCH_TIMEOUT |
No | 30 |
Per-page fetch timeout in seconds |
LLM_TIMEOUT |
No | 60 |
LLM request timeout in seconds |
MAX_CONTENT_SIZE |
No | 5242880 |
Max content size in bytes (5MB) |
DEFAULT_MAX_RESULTS |
No | 10 |
Default result count for web_search |
VLM Configuration (for image-description tool)
| Variable | Required | Default | Description |
|---|---|---|---|
VLM_BASE_URL |
No | LLM_BASE_URL |
OpenAI-compatible endpoint for VLM |
VLM_API_KEY |
No | LLM_API_KEY |
API key for VLM endpoint |
VLM_MODEL |
No | LLM_MODEL |
Model name for image description |
MAX_IMAGE_SIZE |
No | 10485760 |
Max image size in bytes (10MB) |
Agent Configuration
Claude Desktop (stdio)
{
"mcpServers": {
"websearch": {
"command": "uvx",
"args": ["--from", "git+https://github.com/<org>/websearch-mcp", "websearch-mcp"],
"env": {
"SEARXNG_URL": "http://localhost:8888",
"LLM_BASE_URL": "http://localhost:11434/v1",
"LLM_API_KEY": "ollama",
"LLM_MODEL": "llama3"
}
}
}
}
Generic MCP Config (stdio)
{
"command": "uvx",
"args": ["--from", "git+https://github.com/<org>/websearch-mcp", "websearch-mcp"],
"env": {
"SEARXNG_URL": "http://localhost:8888",
"LLM_BASE_URL": "https://api.openai.com/v1",
"LLM_API_KEY": "sk-...",
"LLM_MODEL": "gpt-4o-mini"
}
}
HTTP Transport
websearch-mcp --transport http --port 3000
{
"url": "http://localhost:3000/mcp"
}
Development
uv sync
uv run pytest tests/ -v
Example Usage
image-description tool
With base64-encoded image:
# Using base64 encoded image data
image_b64 = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9QDwADhgGAWjR9awAAAABJRU5ErkJggg=="
result = await image_description(image_b64)
# Returns: {"description": "A small white square", "success": true, "error": null}
With filesystem path:
# Using absolute filesystem path
result = await image_description("/path/to/image.png")
# Returns: {"description": "A detailed description of the image", "success": true, "error": null}
With Ollama (using llava or other VLM):
{
"mcpServers": {
"websearch": {
"command": "uvx",
"args": ["--from", "git+https://github.com/<org>/websearch-mcp", "websearch-mcp"],
"env": {
"SEARXNG_URL": "http://localhost:8888",
"LLM_BASE_URL": "http://localhost:11434/v1",
"LLM_API_KEY": "ollama",
"LLM_MODEL": "llama3",
"VLM_BASE_URL": "http://localhost:11434/v1",
"VLM_API_KEY": "ollama",
"VLM_MODEL": "llava"
}
}
}
}
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。