Solr MCP Server

Overview

The Solr MCP server bridges the gap between large language models and enterprise search by exposing Apache Solr’s powerful document‑retrieval capabilities through the Model Context Protocol. In practice, this means an LLM can ask a question and receive relevant documents pulled directly from a Solr index, all without leaving the conversational context. The server implements both resources and tools: resources provide read‑only access to indexed documents, while tools enable more complex operations such as advanced filtering, sorting, and pagination.

For developers building AI‑powered applications, this MCP server offers a clean, standardized interface that eliminates the need to write custom adapters for Solr. The server’s asynchronous httpx client ensures low‑latency queries, and the use of Pydantic models guarantees that request and response payloads are type‑safe. Authentication is handled via JWT, so the same security model used across other MCP services can protect Solr queries.

Key capabilities include:

• Simple and complex search: Run plain text or structured Solr queries.
• Document retrieval by ID: Fetch a specific document in constant time.
• Filtering, sorting, and pagination: Control result sets directly from the LLM’s prompt.
• Asynchronous communication: Non‑blocking queries that scale with high concurrency.
• Docker‑based development environment: Spin up a ready‑to‑use Solr instance with sample data for rapid prototyping.

Typical use cases are plentiful. A customer support chatbot can pull the most relevant knowledge‑base articles to answer user queries. An internal research assistant can surface policy documents or technical specifications that match a natural‑language request. In an e‑commerce setting, product search results can be returned to a conversational interface, allowing users to refine their queries on the fly.

Integration into AI workflows is straightforward. Once the MCP server is running, an LLM client can invoke the endpoint or reference a resource such as directly in its prompt. The server handles the communication with Solr, returns structured results, and feeds them back into the model’s context. Because the server follows MCP 1.6.0 specifications, it can be swapped out or upgraded without touching the client logic.

What sets Solr MCP apart is its combination of a mature search engine with a modern, protocol‑driven interface. Developers benefit from Solr’s proven scalability and rich query language while enjoying the simplicity of MCP‑style tooling. This makes it an ideal component for any AI system that requires reliable, fast access to large document collections.