Solr Search MCP Server

What does “Solr Search” MCP Server do?

The Solr Search MCP Server serves as an integration layer between Large Language Models (LLMs) and Apache Solr, a powerful open-source search platform. By leveraging the Model Context Protocol (MCP), this server allows AI assistants to search, retrieve, and interact with documents stored in Solr collections. It exposes Solr’s search and retrieval capabilities as standardized resources and tools, enabling streamlined, type-safe, and authenticated access from client applications. Developers can use this MCP server to empower LLMs with advanced search features, including complex queries, document filtering, sorting, pagination, and direct document retrieval—all within secure, asynchronous workflows. This enhances development workflows by making enterprise-grade search available to AI-driven systems.

List of Prompts

No explicit prompt templates are mentioned in the available documentation or repository files.

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List of Resources

• Solr Document Search Resource
  Exposes Solr collections to MCP clients, allowing them to perform searches across indexed documents.
• Document Retrieval Resource
  Enables retrieval of specific documents by their unique IDs from Solr collections.
• Filtered and Sorted Results Resource
  Provides advanced filtering and sorting on search queries to refine and organize results.
• Paginated Search Results Resource
  Supports pagination for large search result sets, so clients can access results in manageable chunks.

List of Tools

• Advanced Search Tool
  Allows LLMs to perform complex Solr queries via the MCP interface, supporting various search parameters.
• Document Fetch Tool
  Enables LLMs to fetch full document details by document ID from Solr.
• Asynchronous Query Execution Tool
  Communicates with Solr in a non-blocking fashion for efficient search and retrieval.
• Authentication Tool (JWT)
  Provides secure, authenticated access to Solr endpoints through the MCP server.

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Use Cases of this MCP Server

• Enterprise Document Search
  Integrate with corporate Solr instances to provide LLM-driven document lookup and knowledge retrieval for support or research teams.
• Codebase Exploration
  Use Solr to index and search code or technical documentation, allowing developers to query and analyze codebases via AI tools.
• API Integration for Smart Assistants
  Equip digital assistants or chatbots with Solr-backed search for answering user queries from large, structured datasets.
• Automated Report Generation
  Retrieve and aggregate data from Solr collections to generate business reports or analytics through AI workflows.
• Secure Content Delivery
  Control access to sensitive documents with JWT authentication, ensuring only authorized LLM interactions with Solr data.

How to set it up

Windsurf

1. Prerequisites: Ensure Python 3.11+, Docker, and uv are installed.
2. Locate Configuration: Open your Windsurf configuration file.
3. Add Solr Search MCP Server: Insert or update the mcpServers object with the Solr Search MCP configuration.
4. Save and Restart: Save the configuration and restart Windsurf.
5. Verify Setup: Confirm the server is running and accessible.

{
  "mcpServers": {
    "solr-search": {
      "command": "python",
      "args": ["run_server.py"]
    }
  }
}

Securing API Keys

Use environment variables for sensitive data (e.g., JWT secrets).
Example:

{
  "mcpServers": {
    "solr-search": {
      "command": "python",
      "args": ["run_server.py"],
      "env": {
        "JWT_SECRET": "${JWT_SECRET}"
      },
      "inputs": {
        "solr_url": "http://localhost:8983/solr"
      }
    }
  }
}

Claude

1. Prerequisites: Install required dependencies and Solr.
2. Edit Configuration: Open Claude’s MCP server config file.
3. Add Server Entry: Insert the MCP server command and args.
4. Restart Claude: Reload or restart the Claude backend.
5. Test Connection: Check for successful MCP handshake.

{
  "mcpServers": {
    "solr-search": {
      "command": "python",
      "args": ["run_server.py"]
    }
  }
}

Cursor

1. Install Dependencies: Ensure Python 3.11+ and Docker are set up.
2. Access Cursor Configuration: Locate the MCP servers section.
3. Insert MCP Server Config: Add Solr Search details.
4. Apply Changes: Save and relaunch Cursor.
5. Validate: Confirm operational status.

{
  "mcpServers": {
    "solr-search": {
      "command": "python",
      "args": ["run_server.py"]
    }
  }
}

Cline

1. Prepare Environment: Python, Docker, and uv must be installed.
2. Open Cline Config: Find the MCP server configuration block.
3. Configure Server: Add details for Solr Search MCP.
4. Restart Cline: Apply configuration changes.
5. Check Integration: Run a test query.

{
  "mcpServers": {
    "solr-search": {
      "command": "python",
      "args": ["run_server.py"]
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "solr-search": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “solr-search” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

My opinion: This MCP server provides robust Solr integration and implements all the basics for secure, type-safe, and flexible document search. However, it lacks explicit prompt templates and makes no mention of Roots or sampling support, which could restrict advanced MCP client workflows. Documentation is solid for setup and functionality but light on deep MCP-specific features.

MCP Score

Rating:
Based on the tables above, I would rate this MCP server a 6/10. It is functional and well-integrated with Solr, but lacks some MCP ecosystem features (like roots, sampling, prompt templates), and does not have a clear open source license.