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Search documents with hybrid retrieval, reranking, guardrails, and tracing.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "hybrid-rag-mcp" yet — see the docs or source repo.
Use search_docs to find how to configure hybrid retrieval and cross-encoder reranking in the project docs, then return the most relevant conclusions.
Returns the most relevant document passages or a summary about the configuration steps.
Use search_docs to search for prompt-injection protections and evaluation mechanisms in the docs, then summarize them as bullet points.
Outputs key points about guardrails and evaluation, grounded in relevant documentation.
Use search_docs to find documentation related to OpenTelemetry tracing and list the results by relevance.
Provides tracing-related documentation results or summaries ranked by relevance.
Developers building document-based QA workflows with MCP can use it for hybrid retrieval and reranking to improve relevance. It fits AI applications that need a more robust retrieval pipeline.
When teams are concerned about prompt injection affecting retrieval or QA results, they can use this tool's guardrails to improve safety. It is useful in scenarios with explicit security requirements for document search.
Teams that need to monitor retrieval performance can use its evaluation and OpenTelemetry tracing features to inspect quality and execution flows. It is well suited to development environments that value observability and validation.
It is an MCP server that provides a search_docs tool for searching documents. It combines BM25, dense vector retrieval, and cross-encoder reranking to improve relevance.
The provided information says it includes evaluation, prompt-injection guardrails, and OpenTelemetry tracing. That means it focuses on retrieval quality as well as safety and observability.
The provided material does not include installation steps, runtime requirements, or key information. Please see the source repository for exact prerequisites.
Use RAG tools for knowledge retrieval, document management, and search visualization.
Retrieve and process docs with vector search to enrich AI responses.
Search markdown knowledge bases with hybrid ranking and intelligent reranking.
Search locally indexed PDF docs with citations and context for LLM agents.
Run hybrid search over self-hosted public data indexes via MCP.
Turn unstructured documents into a searchable knowledge base for AI agents.