Solr Vector Search
Solr MCP provides a bridge between AI assistants and Apache Solr search indexes, enabling powerful hybrid search capabilities that combine keyword precision with vector semantic understanding. Built by Allen Day, this Python implementation uses FastMCP to expose Solr's search functionality through a standardized protocol, with features including vector embeddings generation via Ollama (using nomic-embed-text), unified collections for storing both document content and embeddings, and Docker integration for easy deployment. The server is particularly valuable for workflows requiring advanced document retrieval from existing Solr indexes, allowing AI assistants to perform contextual searches against structured data repositories without direct database access.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-28.
Scan details: Circle-IR · 2026-09-28 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- database
- Source
- PulseMCP
- Repository
- github.com/allenday/solr-mcp
- Author type
- human
- Last scanned
- 2026-09-28
- Updated
- 2026-09-28
Use via MCP
Resolve Solr Vector Search from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.