Skip to main content
Glama
OJamals

LangSearch MCP Server

by OJamals
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

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

Setup

  1. Clone or navigate to the repository:

cd langsearch-mcp-python

  1. Install dependencies:

uv sync

  1. 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:

Environment Variables

Variable

Required

Description

LANGSEARCH_API_KEY

Yes

Your LangSearch API key

License

MIT

Support

For issues and questions:

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

This server cannot be installed

-
security - not tested
F
license - not found
-
quality - not tested

How are these scores calculated?

Resources

New MCP Servers

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/OJamals/langsearch-mcp-python'

If you have feedback or need assistance with the MCP directory API, please join our Discord server