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Local-first MCP retrieval for indexing files and finding relevant passages.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "rag-retriever-mcp" yet — see the docs or source repo.
Index the documents in this project directory and show me how to search for relevant passages by topic later.
A confirmation that indexing is complete, plus the retrieval method or a brief overview of searchable results.
Search the indexed files for passages most relevant to "causes of permission configuration failures" and rank them by relevance.
Several top-ranked document passages that can be used as evidence for the model's final answer.
First retrieve content related to "how the system handles uploaded files," then produce a concise answer based on those passages.
Relevant context passages are retrieved first, followed by an answer grounded in those passages.
When developers or researchers want an agent to answer questions from local files, they can index the files, retrieve relevant passages, and let the agent’s own LLM generate the answer.
When working with many project documents, users can search for the most relevant passages to the current task and reduce manual lookup time.
If an MCP-compatible agent needs document retrieval capabilities, this tool can be mounted as a local-first retrieval engine.
It is a local-first document retrieval engine that mounts as an MCP tool for agents to index files, search relevant passages, and let the agent’s own LLM answer.
Based on the description, it handles indexing and retrieval of relevant passages; the final answer is produced by the agent’s own LLM.
The provided material does not include installation steps, runtime details, or key requirements. See the source repository for specifics.
Index local documents and run hybrid semantic-keyword search on-device.
Search locally indexed PDF docs with citations and context for LLM agents.
Use RAG tools for knowledge retrieval, document management, and search visualization.
Turn unstructured documents into a searchable knowledge base for AI agents.
Let AI securely query private local documents with persistent memory.
Index documents and retrieve relevant context for better LLM responses.