iflow-mcp_divar-ir_sourcegraph-mcp 0.1.0

Model Context Protocol (MCP) server for Sourcegraph code search

Sourcegraph MCP Server

A Model Context Protocol (MCP) server that provides AI-enhanced code search capabilities using Sourcegraph.

Table of Contents

• Overview
• Features
• Prerequisites
• Installation
  • Using UV (recommended)
  • Using pip
  • Using Docker
• Configuration
  • Required Environment Variables
  • Optional Environment Variables
• Usage with AI Tools
  • Cursor
• MCP Tools
  • search
  • search_prompt_guide
  • fetch_content
• Development
  • Linting and Formatting

Overview

This MCP server integrates with Sourcegraph, a universal code search platform that enables searching across multiple repositories and codebases. It provides powerful search capabilities with advanced query syntax, making it ideal for AI assistants that need to find and understand code patterns across large codebases.

Features

• Code Search: Search across codebases using Sourcegraph's powerful query language
• Advanced Query Language: Support for regex patterns, file filters, language filters, and boolean operators
• Repository Discovery: Find repositories by name and explore their structure
• Content Fetching: Browse repository files and directories
• AI Integration: Designed for LLM integration with guided search prompts

Prerequisites

• Sourcegraph Instance: Access to a Sourcegraph instance (either sourcegraph.com or self-hosted)
• Python 3.10+: Required for running the MCP server
• UV (optional): Modern Python package manager for easier dependency management

Installation

Using UV (recommended)

# Install dependencies
uv sync

# Run the server
uv run python -m src.main

Using pip

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install package
pip install -e .

# Run the server
python -m src.main

Using Docker

# Build the image
docker build -t sourcegraph-mcp .

# Run the container with default ports
docker run -p 8000:8000 -p 8080:8080 \
  -e SRC_ENDPOINT=https://sourcegraph.com \
  -e SRC_ACCESS_TOKEN=your-token \
  sourcegraph-mcp

# Or run with custom ports
docker run -p 9000:9000 -p 9080:9080 \
  -e SRC_ENDPOINT=https://sourcegraph.com \
  -e SRC_ACCESS_TOKEN=your-token \
  -e MCP_SSE_PORT=9000 \
  -e MCP_STREAMABLE_HTTP_PORT=9080 \
  sourcegraph-mcp

Configuration

Required Environment Variables

• SRC_ENDPOINT: Sourcegraph instance URL (e.g., https://sourcegraph.com)

Optional Environment Variables

• SRC_ACCESS_TOKEN: Authentication token for private Sourcegraph instances
• MCP_SSE_PORT: SSE server port (default: 8000)
• MCP_STREAMABLE_HTTP_PORT: HTTP server port (default: 8080)

Usage with AI Tools

Cursor

After running the MCP server, add the following to your .cursor/mcp.json file:

{
  "mcpServers": {
    "sourcegraph": {
      "url": "http://localhost:8080/sourcegraph/mcp/"
    }
   }
}

MCP Tools

This server provides three powerful tools for AI assistants:

🔍 search

Search across codebases using Sourcegraph's advanced query syntax with support for regex, language filters, and boolean operators.

📖 search_prompt_guide

Generate a context-aware guide for constructing effective search queries based on your specific objective.

📂 fetch_content

Retrieve file contents or explore directory structures from repositories.

Development

Linting and Formatting

# Check code style
uv run ruff check src/

# Format code
uv run ruff format src/