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
- Clone or navigate to the repository:
cd langsearch-mcp-python
- Install dependencies:
uv sync
- 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 querycount(integer, default=10): Number of results (1-50)summary(boolean, default=true): Include full page summariesfreshness(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 rankingdocuments(array[string], required): List of document texts to reranktop_n(integer, optional): Return only top N resultsmodel(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:
Environment Variables
| Variable | Required | Description |
|---|---|---|
LANGSEARCH_API_KEY | Yes | Your LangSearch API key |
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