Which integrations are available for this server?

Integrates with Ollama's OpenAI-compatible endpoints to provide LLM-based synthesis of web content and image description capabilities via vision models. Supports using OpenAI chat and vision models for processing web search results and generating descriptions for images. Provides web search capabilities by querying SearXNG instances, allowing for result filtering and content synthesis from search result pages.

How do I use websearch-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@websearch-mcp search for the latest news about SpaceX Starship and summarize it" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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] in settings.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"
      }
    }
  }
}

websearch-mcp

websearch-mcp

Prerequisites

Installation

Tools

`web_search`

`webfetch`

`image-description`

Environment Variables

VLM Configuration (for image-description tool)

Agent Configuration

Claude Desktop (stdio)

Generic MCP Config (stdio)

HTTP Transport

Development

Example Usage

image-description tool

Resources

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