by PatrickSys · MCP Server · ★ 57
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codebase-context Map your team's conventions before your AI agent starts searching. You're tired of AI agents writing code that "just works" but still misses how your team actually builds things. They search too broadly, pick generic examples, and spend tokens exploring before they understand the shape of the repo. changes the first step. Start with a bounded conventions map that shows the architecture, dominant patterns, and strongest local examples. Then search for the exact file, symbol, or workflow you need. Here's what codebase-context does: Starts with a bounded conventions map - The first call shows architecture layers, active patterns, golden files, and next calls without dumping vendored repos, fixtures, generated output, or oversized entrypoint lists into the default surface. Finds the right local example - Search does not just return code. Each result comes back with pattern signals, file relationships, and quality indicators so the agent can move from the map to the most relevant local example instead of wandering through raw hits. Knows what is current - Conventions are detected from your code and git history, n
| Stars | 57 |
| Forks | 13 |
| Language | TypeScript |
| Category | MCP Server |
| Quality Score | 69.6638293740239/100 |
| Open Issues | 5 |
| Last Updated | 2026-07-25 |
| Created | 2025-11-24 |
| Platforms | claude-code, mcp, node |
| Est. Tokens | ~17k |
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codebase-context is Codebase Context gives AI agents understanding of your codebase through semantic code search, team conventions, patterns, and memory, so they use fewer tokens, spend less time, and produce better, mor. It is categorized as a MCP Server with 57 GitHub stars.
codebase-context is primarily written in TypeScript. It covers topics such as ai-agents, ai-coding, claude.
You can find installation instructions and usage details in the codebase-context GitHub repository at github.com/PatrickSys/codebase-context. The project has 57 stars and 13 forks, indicating an active community.
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