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Search local documents by keyword, fetch passages, and list sources offline.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "corpus-mcp" yet — see the docs or source repo.
Search the local document directory for the keyword "vector database" and list the most relevant source files.
A list of source documents matching the keyword for further review.
First search for "privacy policy", then fetch the key passages from the most relevant documents.
Relevant original passages are returned so you can quickly read the important parts.
List all source files currently available in the local document directory for search.
A searchable source list is shown so you can confirm the document scope.
Researchers or students can search a local directory of papers, notes, or reference files by keyword to quickly find relevant sources and passages. It is useful for offline review and fact-finding.
Writers or content creators can search their local document library for topic keywords and retrieve relevant text passages and sources. This helps them reuse existing material faster and verify references.
Developers can plug it into an MCP workflow so AI can run keyword search, fetch passages, and list sources from a local document directory. The process does not require external APIs or models.
It is an MCP server for keyword search over a local directory of documents. It can also fetch relevant passages and list the source files for matching content.
No. The description explicitly says it works without requiring external APIs or models.
The provided material does not include installation steps or configuration details. Please see the source repository for specifics.
Index local documents and run hybrid semantic-keyword search on-device.
Search official library docs and return clean text ready for LLM use.
Local-first MCP server for web search and developer documentation fetching.
Search keywords in text files via MCP and return matching results fast.
Search a personal document collection semantically and retrieve full source documents.
Search papers, parse full-text PDFs, extract details, and manage citations.