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Search local markdown vector stores with LLMs and evaluate retrieval quality.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-ai-workspace" yet — see the docs or source repo.
Use mcp-ai-workspace to search the local Markdown vector store for "how to configure authentication for an MCP service", return the 5 most relevant results, and include brief summaries.
Relevant document snippets, source information, and short summaries to help locate the answer quickly.
Run the retrieval evaluation in mcp-ai-workspace for this query set, calculate hit rate and MRR, and identify which queries have poor recall.
Hit rate, MRR, per-query performance, and a list of retrieval issues that need optimization.
Use mcp-ai-workspace to search for "vector index update workflow". If the results are poor, analyze whether the issue is missing documents, chunking, or embedding-retrieval mismatch.
An assessment of result quality plus likely causes and recommendations for improvement.
Chat with AI to retrieve documents and trigger MCP-powered tools.
Search local Markdown files and return full document contents for use.
Use MCP to query knowledge hubs, search collections, and summarize documents.
Turn unstructured documents into a searchable knowledge base for AI agents.
Make MDX documentation searchable and answerable for AI assistants.
Give AI agents MCP tools for task management and document retrieval.