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How Developers Built a Custom MCP Server to Give AI Agents a Codebase Map

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Developers at DEV Community built a custom Model Context Protocol (MCP) server called codebase-memory-mcp to improve how AI agents navigate large codebases. MCP, based on JSON-RPC 2.0, standardizes how AI models discover and call tools, replacing fragmented workarounds like ad-hoc API wrappers and prompt-stuffing techniques. The team used Python and constructed a hybrid SQLite index with three layers — file metadata, symbol records, and chunked text embeddings — after finding that vector search alone produced inaccurate results. They exposed seven carefully chosen tools, including semantic search, exact symbol lookup, file structure browsing, cross-file reference finding, and a persistent memory store for design notes. The structured toolkit was designed to match specific categories of agent failure, ensuring models use precise lookups when possible and fuzzy search only when necessary.

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How Developers Built a Custom MCP Server to Give AI Agents a Codebase Map · ShortSingh