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mrjoshuak/godoc-mcp

by Various

MCP server that reduces LLM context usage when AI coding agents work with Go — structured documentation instead of source-file dumps.

M

MCP

mrjoshuak/godoc-mcp

Added 1 June 2026

#ai-agents #ai-tools #anthropic #claude #documentation #golang #llm #llm-tools

Overview

MCP server that reduces LLM context usage when AI coding agents work with Go. It provides structured documentation instead of source-file dumps.

Best for

Best for
Developers using AI coding agents with Go projects to minimize context overhead

Use cases

  • Feeding Go package documentation to MCP-compatible AI agents
  • Reducing token usage during Go codebase analysis in LLMs
  • Replacing source file dumps with compact doc summaries for coding assistants

How to use

Install

go install github.com/mrjoshuak/godoc-mcp@latest

Tools exposed

  • get_doc
  • list_packages

Tested with

Claude Desktop, Claude Code

Example client config

{\n  "mcpServers": {\n    "godoc": {\n      "command": "godoc-mcp"\n    }\n  }\n}

Notes

MCP server that reduces LLM context usage when AI coding agents work with Go. It provides structured documentation instead of source-file dumps.

120 stars on GitHub. Last updated 2026-03-06. Licensed MIT.

Use cases

  • Feeding Go package documentation to MCP-compatible AI agents
  • Reducing token usage during Go codebase analysis in LLMs
  • Replacing source file dumps with compact doc summaries for coding assistants

Pros

  • Efficiently lowers token consumption for LLM interactions with Go code
  • Structured documentation output improves relevance over raw source
  • Lightweight MCP server written in Go for easy integration

Cons

  • Limited to Go documentation only; no support for other languages
  • Requires MCP protocol support in the AI agent or client
  • May miss implementation details not covered in documentation

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Efficiently lowers token consumption for LLM interactions with Go code
  • Structured documentation output improves relevance over raw source
  • Lightweight MCP server written in Go for easy integration

Cons

  • Limited to Go documentation only; no support for other languages
  • Requires MCP protocol support in the AI agent or client
  • May miss implementation details not covered in documentation
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