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.
MCP
mrjoshuak/godoc-mcp
Added 1 June 2026
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_doclist_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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