GitHub Agentic Chat MCP Server: Scalable, Smarter Collaboration

What is GitHub Agentic Chat MCP Server: Scalable, Smarter Collaboration?

GitHub Agentic Chat MCP Server is an open-source implementation of the Model Context Protocol (MCP) built with Go. It enables natural language interaction with GitHub repositories and integrates vector search capabilities to power semantic document analysis. This server acts as a bridge between AI clients like Claude Desktop and GitHub APIs, streamlining workflows for developers and teams through intelligent automation.

How to Use GitHub Agentic Chat MCP Server: Scalable, Smarter Collaboration?

1. Install dependencies: Go 1.21+, PostgreSQL with pgvector, and API keys
2. Clone repository: git clone https://github.com/akhidasTech/github-agentic-chat-mcp.git
3. Configure environment variables for GitHub, OpenAI, and database access
4. Initialize PostgreSQL with vector extensions: CREATE EXTENSION vector;
5. Build the server executable with go build
6. Register server in client config (e.g., Claude Desktop's mcpServers block)

Key Features of GitHub Agentic Chat MCP Server: Scalable, Smarter Collaboration?

• GitHub Integration: Search repositories and create issues via natural language commands
• Semantic Search Engine: Store documents with metadata and perform vector-based similarity searches
• Modular Architecture: Extensible framework for adding custom tools and integrations
• Production-Ready: Built with Go for high performance and concurrency handling

Use Cases of GitHub Agentic Chat MCP Server: Scalable, Smarter Collaboration?

Practical applications include:

• Automating issue creation based on natural language requests
• Searching codebases and documentation using semantic queries
• Building chatbots for repository management workflows
• Creating knowledge bases with contextual document retrieval
• Integrating with AI clients for advanced GitHub operations

FAQ from GitHub Agentic Chat MCP Server: Scalable, Smarter Collaboration?

• Q: What databases are supported?
  PostgreSQL with pgvector extension is required for vector storage
• Q: Can I use other LLMs?
  Yes, any MCP-compatible client can connect including Claude, OpenAI, or custom models
• Q: How do I add new tools?
  Implement Tool interface and register in server configuration
• Q: Is authentication mandatory?
  GitHub and OpenAI API keys are required for full functionality
• Q: What's the performance like?
  Built with Go's concurrency model for handling thousands of requests per second

GitHub Agentic Chat MCP Server

This is an MCP (Model Context Protocol) server implementation for GitHub agentic chat using Go. It provides tools for interacting with GitHub through natural language and includes vector search capabilities.

Features

• Search GitHub repositories
• Create issues
• Vector search functionality
  • Add documents to vector store
  • Semantic search across stored documents
• Extensible structure for adding more features

Prerequisites

• Go 1.21 or later
• PostgreSQL with pgvector extension
• GitHub Personal Access Token
• OpenAI API Key
• Claude Desktop or other MCP-compatible client

Setup

1. Clone the repository:

git clone https://github.com/akhidasTech/github-agentic-chat-mcp.git
cd github-agentic-chat-mcp

2. Set up environment variables:

export GITHUB_TOKEN=your_github_token_here
export DATABASE_URL=postgres://user:password@localhost:5432/dbname
export OPENAI_API_KEY=your_openai_api_key_here

3. Set up PostgreSQL with pgvector:

CREATE EXTENSION vector;

4. Build the server:

go build -o bin/github-agentic-chat-mcp

5. Configure Claude Desktop: Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{
    "mcpServers": {
        "github-chat": {
            "command": "/absolute/path/to/bin/github-agentic-chat-mcp"
        }
    }
}

6. Restart Claude Desktop

Available Tools

GitHub Tools

search_repositories

Search for GitHub repositories using a query string.

create_issue

Create a new issue in a GitHub repository.

Vector Search Tools

add_to_vector_store

Add a document to the vector store for semantic search.

Parameters:

• content: The text content to store
• metadata: JSON string containing metadata about the content

Example:

{
    "content": "This is a document about GitHub Actions",
    "metadata": "{\"type\": \"documentation\", \"tags\": [\"github\", \"ci-cd\"]}"
}

vector_search

Perform semantic search across stored documents.

Parameters:

• query: The search query text
• limit: Maximum number of results to return (default: 5)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License