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Securely read files, browse directories, and run filtered RAG knowledge searches.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "cursor-context-mcp" yet — see the docs or source repo.
Use cursor-context-mcp to search within pack_id=frontend-docs and case_id=case-104 for content related to "login failure retry mechanism," and return the 5 most relevant results with file paths.
Filtered relevant document snippets, source paths, and brief relevance notes.
Use cursor-context-mcp to read config/app.yaml within the allowlist and summarize its database, cache, and logging settings.
A structured summary of the config file with key parameters grouped by module.
Use cursor-context-mcp to list files and subdirectories under docs/architecture and identify the file most likely to contain content about "event bus design."
A directory listing plus the candidate file and the reasoning behind the choice.
Index PDFs into Qdrant and enable semantic search and RAG document QA.
Retrieve relevant context and metadata from Qdrant using natural language queries.
Query and manage LlamaIndex documents stored in Qdrant vector databases.
Turn unstructured documents into a searchable knowledge base for AI agents.
Production-ready MCP server for query normalization, retrieval, and RAG prompt building.
Give AI coding agents persistent semantic memory and workspace-aware code search.