An autonomous MCP agent that provides token-efficient code analysis, documentation retrieval, and implementation guidance for large library repositories using a dedicated OpenAI-compatible LLM.
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      [](https://github.com/punkpeye/awesome-mcp-servers) [](https://glama.ai/mcp/servers/FI-Mihej/codebase-agent-mcp) # CodebaseAgent-MCP CodebaseAgent-MCP is a token-efficient MCP server for AI coding agents that delegates large codebase analysis to a dedicated OpenAI-compatible LLM, reducing context size, latency, and token costs. Instead of forcing your primary coding assistant to repeatedly scan large codebases, it performs that work separately and returns only the information needed for the current task. It can run against local models such as Gemma or Qwen, as well as inexpensive OpenAI-compatible cloud providers, reducing both latency and token consumption while keeping the primary assistant focused on reasoning and code generation. Optional Qdrant integration can cache previous retrieval results today and is planned to evolve into semantic retrieval of code entities (files, classes and functions) from connected codebases. ## Subharness features - Designed specifically for managing small LLMs: it focuses on addressing their specific behavioral characteristics and weaknesses that prevent other harnesses from working with them effectively. - Precise conversation-history compaction. - LLM anti-stuck mechanism (prevents the model from falling in…
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