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Index source code locally and search it semantically with natural-language queries.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Zvec MCP Bridge" yet — see the docs or source repo.
Search the indexed project code for implementations related to user login, token validation, and permission checks. List the key files, functions, and what they do.
Returns relevant source locations, core function explanations, and surrounding context for further investigation.
Find the main entry points for the "export report" feature in the project. Explain the frontend trigger, backend flow, and relationships between related modules.
Outputs entry files, a summarized call chain, and module relationships to help quickly understand the implementation path.
Search the current codebase for implementations similar to "automatically reloading data after cache invalidation" and rank the results by relevance.
Provides semantically similar code snippets, their file locations, and why they are similar for reuse or comparison.
Build semantic indexes for codebases to find relevant code with natural language queries.
Connect AI agents to secure RAG workflows across multiple vector databases.
Incrementally index repos and documents, then run semantic search over them.
Index and AI-search Obsidian notes through MCP with vector embeddings.
Embed your codebase for fast semantic search with Graph RAG.
Build persistent, semantically searchable memory for codebases via natural language queries.