$ loading_
Index local documents and run hybrid semantic-keyword search on-device.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "minirag-mcp" yet — see the docs or source repo.
Please index the documents in this project folder and explain how I can search them later by both semantics and keywords.
A local document index is created, along with guidance for later semantic and keyword search.
Please fetch this webpage, add it to the local knowledge base, and then find the passages most relevant to “deployment steps.”
The webpage content is fetched, indexed, and the most relevant results for the query are returned.
Search the indexed materials for content related to “error handling” and “exception recovery,” prioritizing results that are semantically relevant and contain the keywords.
Relevant document snippets or result lists combining semantic and keyword matches are returned.
Developers or researchers can index local files first, then quickly locate information with hybrid semantic and keyword search. Because data stays on-device, it suits local document collections.
When information is split across local files and webpages, users can fetch URL content and include it in the same search flow. This enables unified lookup in a local-first knowledge base.
Users who prefer command-line workflows can use the CLI for document ingestion, indexing, and search. It fits well into local development or research workflows.
It is a local-first RAG MCP server that indexes local documents and provides hybrid search combining semantic and keyword retrieval. It also supports file ingestion, URL fetching, and a CLI.
Based on the description, it emphasizes keeping data on-device. For implementation details, see the source repository.
The provided material only says it offers an MCP server and a CLI, but does not specify installation steps, runtime requirements, or API keys. See the source repository for exact prerequisites.
Search code and technical docs privately with local-first RAG for developers.
Local-first MCP retrieval for indexing files and finding relevant passages.
Index local files for Claude to search and cite internal documents.
Index a knowledge base into Chroma and retrieve relevant document fragments.
Search local documents by keyword, fetch passages, and list sources offline.
Retrieve relevant document chunks and generate suggested LLM prompts via REST and MCP.