Meilisearch Hybrid Search MCP Server

The Meilisearch Hybrid Search MCP Server bridges the gap between traditional keyword search and modern semantic retrieval by exposing a single, powerful tool to AI assistants. It enables developers to tap into Meilisearch’s fast inverted‑index capabilities while also leveraging vector embeddings for contextually relevant results. This hybrid approach is especially valuable when working with large corpora that contain both structured metadata and unstructured text, allowing an assistant to answer queries accurately without sacrificing speed.

What Problem Does It Solve?

Many AI applications require quick access to indexed documents, but pure keyword matching often misses semantically related content. Conversely, vector‑only searches can be slow and lack the precision of keyword filtering. This server solves both issues by combining the strengths of Meilisearch’s efficient text search with its optional semantic vector search. Developers no longer need to maintain separate pipelines for keyword and embedding queries; instead, they can issue a single request that balances both modalities according to the needs of the conversation.

How It Works and Why It Matters

The server exposes a tool that accepts a keyword query, an optional semantic ratio, and filter parameters. Internally it forwards the request to Meilisearch’s hybrid search endpoint, which blends a BM25 keyword score with a cosine similarity score from the configured embedder. The parameter lets developers fine‑tune the balance—0 for pure keyword, 1 for pure semantic, or any value in between. By exposing this through MCP, AI assistants can call the tool directly from prompts, making it trivial to integrate advanced search into dialogue flows.

Key Features

• Hybrid Retrieval: Simultaneous keyword and vector search in a single request.
• Fine‑Tuned Relevance: Adjustable for context‑specific relevance.
• Filtering Capabilities: Filterable attributes and values to narrow results (e.g., genre, author).
• Environment‑Driven Configuration: Simple environment variables for host, API key, index, embedder, and filterable attributes.
• Standard MCP Interface: Tool available via standard input/output, making it plug‑and‑play with any Claude or other MCP‑compliant assistant.

Use Cases & Real‑World Scenarios

• Customer Support: Quickly surface policy documents that match a user’s question, even if the exact keywords differ.
• Content Recommendation: Suggest articles or books that are semantically similar to a user’s interests while respecting genre filters.
• Enterprise Knowledge Bases: Retrieve policy, code, or documentation snippets that match both keyword and conceptual relevance.
• Multilingual Retrieval: Combine keyword matching in the user’s language with semantic embeddings that bridge cross‑lingual gaps.

Integration Into AI Workflows

Developers can add the tool to an MCP‑enabled assistant and invoke it within prompts. The assistant can then parse the results, rank them further if needed, or pass them to downstream processing steps (e.g., summarization). Because the server listens on standard I/O, it can run as a lightweight microservice alongside other tools, and the environment variables allow seamless deployment across different Meilisearch instances.

By unifying keyword precision with semantic depth, the Meilisearch Hybrid Search MCP Server gives AI assistants a robust, scalable search capability that is both fast and contextually aware—an essential asset for any application that relies on accurate, relevant information retrieval.