LangSearch MCP Server

A Model Context Protocol (MCP) server that provides web search and semantic reranking capabilities using the LangSearch API.

Features

• Web Search: Search billions of web documents with AI-optimized results

  • Full page summaries
  • Freshness filtering (day, week, month)
  • Customizable result count
  • Machine-readable structured output

• Semantic Reranking: Improve search accuracy with deep semantic understanding

  • Reorder documents by semantic relevance
  • Relevance scores (0-1 scale)
  • Better than traditional keyword/vector search
  • Top-N filtering

Installation

Prerequisites

• Python 3.10 or higher
• uv package manager
• LangSearch API key from https://langsearch.com

Setup

1. Clone or navigate to the repository:

cd langsearch-mcp-python

2. Install dependencies:

uv sync

3. Configure your API key:

cp .env.example .env
# Edit .env and add your LANGSEARCH_API_KEY

Usage

Testing with MCP Inspector

Test the server interactively:

uv run mcp dev main.py

This opens the MCP Inspector where you can:

• Browse available tools
• Test tool invocations
• View structured responses

Installing to Claude Desktop

Install the server for use with Claude Desktop:

uv run mcp install main.py

Follow the prompts to configure the installation.

Manual Installation in Claude Desktop

Add to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "langsearch": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/langsearch-mcp-python",
        "run",
        "main.py"
      ],
      "env": {
        "LANGSEARCH_API_KEY": "your_api_key_here"
      }
    }
  }
}

Running as HTTP Server

For remote access, run as an HTTP server:

# In main.py, change the last line to:
if __name__ == "__main__":
    mcp.run(transport="streamable-http")

Then run:

uv run main.py

Access at http://localhost:8000/mcp

Tools

web_search

Search the web for information across billions of documents.

Parameters:

• query (string, required): Search query
• count (integer, default=10): Number of results (1-50)
• summary (boolean, default=true): Include full page summaries
• freshness (string, default="noLimit"): Filter by freshness
  • "noLimit": All results
  • "day": Last 24 hours
  • "week": Last 7 days
  • "month": Last 30 days

Returns: Structured data with:

• Total result count
• Web pages with title, URL, snippet, summary
• Original query

Example:

{
  "query": "latest AI developments 2026",
  "count": 5,
  "summary": true,
  "freshness": "week"
}

semantic_rerank

Rerank documents based on semantic relevance to a query.

Parameters:

• query (string, required): Search query for ranking
• documents (array[string], required): List of document texts to rerank
• top_n (integer, optional): Return only top N results
• model (string, default="langsearch-reranker-v1"): Reranker model

Returns: Structured data with:

• Reranked documents with indices
• Relevance scores (0-1, higher = more relevant)
• Model used

Example:

{
  "query": "machine learning algorithms",
  "documents": [
    "Deep learning is a subset of machine learning...",
    "The history of neural networks dates back...",
    "Random forests are ensemble learning methods..."
  ],
  "top_n": 2
}

Development

Project Structure

langsearch-mcp-python/
├── main.py              # MCP server implementation
├── pyproject.toml       # Project dependencies
├── uv.lock             # Locked dependencies
├── .env.example        # Example environment variables
├── .gitignore          # Git ignore rules
└── README.md           # This file

Running Tests

# Test with MCP Inspector
uv run mcp dev main.py

# Test individual tools
uv run python -c "
from main import web_search
import asyncio
result = asyncio.run(web_search('Python programming', count=3))
print(result)
"

Error Handling

The server provides clear error messages for:

• Missing API key
• Invalid parameters
• API errors
• Network issues

Errors are returned as structured exceptions with descriptive messages.

API Reference

For detailed API documentation, see:

• Web Search API
• Semantic Rerank API

Environment Variables

License

MIT

Support

For issues and questions:

• LangSearch API: docs.langsearch.com
• MCP Protocol: modelcontextprotocol.io

Contributing

Contributions are welcome! Please ensure:

• Type hints are used throughout
• Docstrings follow the existing format
• Error handling is comprehensive
• Tests pass with uv run mcp dev