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apache/solr-mcp

Project Status: Incubating

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, VS Code/Copilot, Cursor, JetBrains) can invoke.

Quick start

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

Compatibility: works with Apache Solr 8.11–10 (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 http://localhost:8983/solr/.

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 Code · VS Code / Copilot · Cursor · JetBrains · 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

Tool Description
search Full-text search with filtering, faceting, sorting, and pagination
index-json-documents Index documents from a JSON string into a collection
index-csv-documents Index documents from a CSV string into a collection
index-xml-documents Index documents from an XML string into a collection
create-collection Create a collection (configSet, numShards, replicationFactor optional — default _default, 1, 1)
list-collections List all available Solr collections
get-collection-stats Get statistics and metrics for a collection
check-health Check the health status of a collection
add-fields Add fields to a collection schema (additive only; existing fields cannot be modified)
add-field-types Add field types — custom analyzers, DenseVectorField for semantic search, etc.
get-schema Retrieve schema information for a collection

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

Resource URI Description
solr://collections List of all Solr collections in the cluster
solr://{collection}/schema Schema definition for a collection (supports autocompletion)

Prompts

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

Prompt Arguments Purpose
explore-collections List collections and characterise each by stats and health
setup-collection name, purpose (optional) Pick configset / shards / replication factor, create the collection, verify it
view-schema collection Read-only schema walkthrough
design-schema collection, datasetDescription, sampleDocument (optional) Choose field types and apply additive schema changes
index-data collection, format (json / csv / xml), sample (optional) Pick the right indexing tool and confirm the result
search-collection collection, question Translate a natural-language question into a Solr query

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.

Configuration

The server reads configuration from environment variables. The essentials:

Variable Description Default
SOLR_URL Solr base URL http://localhost:8983/solr/
PROFILES Transport mode: stdio (default, for Claude Desktop) or http (remote / multi-client) stdio

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

Documentation

Using it

Developing it

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

License

Apache License 2.0 — see LICENSE.

Acknowledgments

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

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