Solr MCP Server

Search, index, and manage Apache Solr collections using natural language — no need to hand-craft Solr queries, build filter expressions, or memorize the admin API.

Instead of writing:

q=title:"star wars" AND genre_s:"sci-fi"&fq=year_i:[2000 TO *]&facet=true&facet.field=genre_s&sort=score desc&rows=10

Just ask your AI assistant:

“Find sci-fi movies with ‘star wars’ in the title released after 2000, show me the genre breakdown, and sort by relevance.”

This Spring AI Model Context Protocol (MCP) server exposes Solr operations as tools that any MCP-compatible AI client (Claude Desktop, Claude Code, Codex, ChatGPT, VS Code/Copilot, Cursor, JetBrains, Zed) can invoke.

Quick start

Prerequisites: Java 25+, Docker and Docker Compose, Git.

Compatibility: works with Apache Solr 9.4–10 (the test suite runs against 9.9 by default — see Solr version compatibility).

1. Start Solr with sample data

git clone https://github.com/apache/solr-mcp.git
cd solr-mcp
docker compose up -d

This starts Solr in SolrCloud mode with two sample collections: films (1,100+ movies) and books (empty, ready for indexing). Wait ~30 seconds, then verify at .

2. Build the server

./gradlew build

This produces build/libs/solr-mcp-1.0.0-SNAPSHOT.jar.

3. Connect your AI client

Add the server to your MCP client. For Claude Desktop, edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows), then restart Claude:

{
  "mcpServers": {
    "solr-mcp": {
      "command": "java",
      "args": ["-jar", "/absolute/path/to/solr-mcp/build/libs/solr-mcp-1.0.0-SNAPSHOT.jar"],
      "env": { "SOLR_URL": "http://localhost:8983/solr/" }
    }
  }
}

Using a different client, or want STDIO/HTTP/Docker options? See the per-client guides: Claude Desktop · Claude Code · Codex · ChatGPT (HTTP only) · VS Code / Copilot · Cursor · JetBrains · Zed · MCP Inspector.

4. Try it out

• “What collections are available in Solr?”
• “Search the films collection for movies directed by Steven Spielberg”
• “Show me the schema for the films collection”
• “Index this JSON into the books collection: [{“id”: “1”, “title”: “The Great Gatsby”, “author”: “F. Scott Fitzgerald”}]”

Example prompts

Searching

• “Find sci-fi movies released after 2000 and show the genre breakdown”
• “Search films for movies with ‘war’ in the title, sorted by year”
• “Show me the top 5 most recent films”

Indexing

• “Index this JSON into the books collection: [{“id”: “1”, “title”: “1984”, “author”: “George Orwell”}]”
• “Create a new collection called products”

Managing

• “Is the films collection healthy?”
• “How many documents are in the films collection?”
• “Show me the schema for the films collection”

What you can do

Tools

Every tool advertises MCP behavior hints (readOnlyHint, destructiveHint, idempotentHint) so clients can build sensible approval UX — search and the metadata tools are read-only, indexing is destructive but idempotent, schema modification is additive.

Resources

Prompts

Slash-command-style workflow templates that walk the assistant through a canonical Solr workflow.

Completions

The server implements MCP argument autocompletion, so clients can suggest valid values as you type:

• Resource argument — the {collection} segment of solr://{collection}/schema completes to live collection names.
• Prompt arguments — the collection argument of the search-collection, index-data, view-schema, and design-schema prompts completes to live collection names.

Suggestions are matched case-insensitively by prefix and capped per request.

Screenshots

Claude Desktop (STDIO) — answering a question about the films collection with the server’s tools:

Configuration

The server reads configuration from environment variables. The essentials:

Running in HTTP mode — OAuth2, CORS, and the HTTP_SECURITY_ENABLED toggle (secured by default) — is covered in the security docs. Tracing, metrics and log-export env vars (OTEL_SAMPLING_PROBABILITY, OTEL_TRACES_URL, OTEL_METRICS_URL, OTEL_LOGS_URL) are covered in Observability.

Documentation

Using it

• Quick start · Client setup — Claude Desktop, Claude Code, Codex, ChatGPT, VS Code, Cursor, JetBrains, Zed, MCP Inspector
• Tutorial: your first collection — index a dataset twice, schemaless then with a designed schema, and see why field types matter
• Observability — OpenTelemetry traces, metrics, logs
• Security: Deployment model (single-tenant) · STDIO model · HTTP model · OAuth2 setup: Auth0 · Keycloak

Developing it

• Development guide — build, run, test, IDE, native image, SBOM · Architecture
• Deployment — Docker images, the three-image matrix, registries, Kubernetes · Troubleshooting
• GraalVM native image spec · Changelog guide · Contributing

Container images: published images are not yet available on a public registry. The Docker examples in the client guides use a locally built image — build it with ./gradlew jibDockerBuild (produces solr-mcp:latest). See Building Docker images.

Community

• Website: https://solr.apache.org/mcp
• Slack: #solr-mcp in the the-asf workspace
• Mailing lists: Shared with Apache Solr — see mailing lists
• Issues: https://github.com/apache/solr-mcp/issues
• Discussions: https://github.com/apache/solr-mcp/discussions

License

Apache License 2.0 — see LICENSE.

Acknowledgments

Built with Spring AI MCP, Apache Solr, Jib, Paketo Buildpacks, Testcontainers, and Spring AI MCP Security.