GitHub Agentic Chat MCP Server
@akhidasTech
About GitHub Agentic Chat MCP Server
An MCP server implementation for GitHub agentic chat using Go
Basic information
Config
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryTools
No tools detected
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is GitHub Agentic Chat MCP Server?
GitHub Agentic Chat MCP Server is a Model Context Protocol (MCP) server written in Go that lets you interact with GitHub through natural language. It provides tools for searching repositories, creating issues, and maintaining a vector store for semantic search over documents. It is designed for developers using MCP‑compatible clients like Claude Desktop.
How to use GitHub Agentic Chat MCP Server?
Set environment variables (GITHUB_TOKEN, DATABASE_URL, OPENAI_API_KEY), build the Go binary, then add a configuration entry in your MCP client (e.g., Claude Desktop’s claude_desktop_config.json) pointing to the compiled binary. After restarting the client, the server’s tools become available for use.
Key features of GitHub Agentic Chat MCP Server
- Search GitHub repositories by query string.
- Create issues in any public repository.
- Add documents to a vector store with JSON metadata.
- Perform semantic vector searches across stored documents.
- Built with Go for performance and extensibility.
Use cases of GitHub Agentic Chat MCP Server
- Ask an AI assistant to find GitHub repositories matching specific criteria.
- Automate issue creation from natural language commands.
- Build a searchable knowledge base of documentation using vector embeddings.
- Enable semantic retrieval of internal notes or code snippets alongside GitHub operations.
FAQ from GitHub Agentic Chat MCP Server
What makes this server different from other GitHub MCP servers?
It combines direct GitHub actions (search, issue creation) with a vector store for semantic search, allowing you to store and retrieve custom documents alongside repository interactions.
What are the runtime requirements?
You need Go 1.21+, a PostgreSQL database with the pgvector extension, a GitHub Personal Access Token, and an OpenAI API Key. The server runs as a local binary.
Where is the vector data stored?
All vector data and documents are stored in your own PostgreSQL database via the pgvector extension. No external cloud storage is used.
How does the server authenticate with GitHub and OpenAI?
Authentication is provided via environment variables: GITHUB_TOKEN for GitHub and OPENAI_API_KEY for OpenAI embeddings.
What transport does the server use?
The server uses the standard MCP stdio transport, designed to be launched by an MCP client like Claude Desktop. It does not expose an HTTP server by default.
More AI & Agents MCP servers
Shell and Coding agent for Claude and other mcp clients
rusiaamanShell and coding agent on mcp clients
欢迎来到 智言平台
Shy2593666979AgentChat 是一个基于 LLM 的智能体交流平台,内置默认 Agent 并支持用户自定义 Agent。通过多轮对话和任务协作,Agent 可以理解并协助完成复杂任务。项目集成 LangChain、Function Call、MCP 协议、RAG、Memory、HITL、Skill、Milvus 和 ElasticSearch 等技术,实现高效的知识检索与工具调用,使用 FastAPI 构建高性能后端服务。
1MCP - One MCP Server for All
1mcp-appA unified Model Context Protocol server implementation that aggregates multiple MCP servers into one.
Sequential Thinking Multi-Agent System (MAS)
FradSerAn advanced sequential thinking process using a Multi-Agent System (MAS) built with the Agno framework and served via MCP.
MCP Claude Code
SDGLBLMCP implementation of Claude Code capabilities and more
Comments