PatrickSys/codebase-context
by Various
Generate a map of your codebaseto help AI Agents understand your architecture, coding conventions and patterns. Discoverable with Semantic Search
MCP
PatrickSys/codebase-context
Added 1 June 2026
Overview
A TypeScript tool that generates a structured map of a codebase, including architecture, coding conventions, and patterns. It uses semantic search to make the map discoverable for AI agents.
Best for
Best for
Developers who want to give AI agents a structured understanding of their codebase's architecture and conventions.
Use cases
- Onboarding AI agents to a new codebase by providing a structured context map
- Enabling semantic search across codebase architecture and conventions
- Standardizing codebase documentation for agent-assisted development
Notes
A TypeScript tool that generates a structured map of a codebase, including architecture, coding conventions, and patterns. It uses semantic search to make the map discoverable for AI agents.
45 stars on GitHub. Last updated 2026-05-11.
Use cases
- Onboarding AI agents to a new codebase by providing a structured context map
- Enabling semantic search across codebase architecture and conventions
- Standardizing codebase documentation for agent-assisted development
Pros
- Semantic search makes codebase context easily discoverable
- Lightweight TypeScript implementation with low overhead
- Focuses on architecture and conventions, not just file listings
Cons
- Small community with only 45 stars, limited support
- Requires manual setup and maintenance of the context map
- May not scale well for very large or rapidly changing codebases
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Semantic search makes codebase context easily discoverable
- Lightweight TypeScript implementation with low overhead
- Focuses on architecture and conventions, not just file listings
Cons
- Small community with only 45 stars, limited support
- Requires manual setup and maintenance of the context map
- May not scale well for very large or rapidly changing codebases
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