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Query private knowledge bases with modular RAG, hybrid retrieval, reranking, and observability.
The materials indicate this MCP tool is open-source under MIT, with no declared secrets or remote endpoints, and no clear high-risk red flags are present. Caution is still warranted because it is flagged as capable of code execution and, as a RAG service, it will likely access local knowledge-base data in a controlled environment.
The materials explicitly state that no keys or environment variables are required, and no API keys, account credentials, or tokens are mentioned; based on the available information, credential exposure and abuse risk appears low.
The materials declare no remote endpoint hosts, and the README provides no indication of external service connections; there is no explicit evidence of user data being sent to third-party or unknown network destinations.
The system has flagged this tool as capable of code execution, indicating it may run a local service or execute code on the host; this is common for MCP tools, but it still warrants restricted privileges and deployment in an isolated environment.
Its description says it queries 'private knowledge bases,' which typically implies access to local or private data sources; the exact read/write scope is not documented, and no clear overprivilege is evident from the materials, but least-privilege data access should be enforced.
Positive signals include open-source availability and an MIT license, which make it auditable; however, it comes from a third-party registry, has 0 GitHub stars, unknown maintenance status, and no README, reducing confidence and verifiability. Review the source code and dependency list before use.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Modular RAG MCP Server" yet — see the docs or source repo.
Use the Modular RAG MCP Server to search my private knowledge base and answer: "What exceptions does our refund policy have for enterprise customers?" Include citations and relevant excerpts.
An answer grounded in the private knowledge base, with cited documents, excerpts, and traceable sources.
Use this MCP tool to run keyword, semantic, and hybrid retrieval for the question: "What were the main changes to the authentication module in the last three releases?" Show the difference before and after reranking.
A comparison of retrieval strategies and a more relevant ranked result list after reranking.
Use the Modular RAG MCP Server to analyze observability data for this query flow: the user asked, "How do I deploy the local agent service?" but the answer was inaccurate. Inspect retrieval, reranking, and final context construction, and identify likely issues.
A diagnostic report explaining whether the issue likely comes from retrieval, reranking, or context assembly.
Build modular RAG workflows for document Q&A, semantic search, and knowledge bases.
Turn unstructured documents into a searchable knowledge base for AI agents.
Expose modular retrieval and reasoning tools to AI assistants through MCP.
Build production-grade RAG systems with hybrid retrieval and agentic reasoning.
Intelligent RAG tool that chooses between private knowledge and web search.
Build advanced RAG retrieval with knowledge graphs, multimodal parsing, and flexible query modes.