opensolr-mcp

mcp-name: com.opensolr/opensolr-mcp

MCP (Model Context Protocol) server for Opensolr — gives any AI agent managed Apache Solr search as tools: hybrid (BM25 + kNN) retrieval, server-side GPU embeddings, document indexing, and grounded RAG answers.

See it live (real news index, hybrid + AI answer): https://search.opensolr.com/news__dense?q=how+am+I+supposed+to+save+money%3F

No embedding model to configure. No vector database to run. One API key.

Tools

Setup

Get a free Opensolr account (15-day trial, no card) at opensolr.com/register and copy your API key from Account.

Claude Desktop / Claude Code

{
  "mcpServers": {
    "opensolr": {
      "command": "uvx",
      "args": ["opensolr-mcp"],
      "env": {
        "OPENSOLR_EMAIL": "you@example.com",
        "OPENSOLR_API_KEY": "YOUR_OPENSOLR_API_KEY"
      }
    }
  }
}

Cursor / Windsurf / any MCP client

Same shape — stdio transport, command uvx opensolr-mcp (or pipx run opensolr-mcp), with OPENSOLR_EMAIL and OPENSOLR_API_KEY in env.

Example agent session

You: Index our FAQ answers, then find everything about refunds.

The agent calls opensolr_add_documents(index="faq__dense", texts=[...]), then opensolr_search(index="faq__dense", query="refund policy", hybrid=True) — BM25 catches the exact word "refund", kNN catches "giving customers their money back", and the scores fuse per document.

Notes

• Vector-enabled indexes run on Opensolr's Solr 9.x environments — currently us (Chicago), de (Germany), fi (Finland), fetched live via opensolr_vector_regions. Additional dedicated regions can be deployed on request (paid add-on): support@opensolr.com.
• Every index is also plain Apache Solr with the native /select API — nothing is locked behind the tools.
• Python sibling for LangChain: langchain-opensolr · Product page: opensolr.com/langchain

How writing works (Data Ingestion API)

Writes go through Opensolr's Data Ingestion API — the same pipeline the Drupal and WordPress connectors use. It is asynchronous: documents are queued, then embeddings, sentiment, language and all crawler-identical derived fields are computed server-side, and documents become searchable within about a minute. Progress is visible in Control Panel → Data Ingestion — a per-job status board (queued / processing / completed / failed, with processed / success / failed document counts per job) — and via the ingest_status API. Each document's identity is its uri (the Solr id is md5(uri)): pass a real URL in metadata ({"uri": "https://..."}), or a deterministic one is synthesized from your id. Re-submitting the same uri updates the document. Pass {"rtf": True, "uri": "https://.../file.pdf"} and the server extracts the text from PDF/DOCX/XLSX for you.

Lexical-only mode

Don't need vectors? Pure keyword search skips the embedding call entirely — zero AI quota, and it works on any Opensolr index, including non-vector ones and older Solr versions.

Your index schema

Documents follow the Opensolr document model (title, description, text, meta_* custom fields). To see the full schema: Control Panel → click your index → Configuration → Edit File → schema.xml. Prefer zero-effort data entry? Configure the Web Crawler in the Control Panel (Index Tools → WebCrawler): add your site URL, validate it, and Opensolr indexes the whole site for you.

Search tuning

Retrieval (search and RAG grounding) runs through the platform's tuned pipeline: global defaults → your index's saved Search Tuning (Control Panel → Index Settings → Search Tuning: semantic↔lexical balance, field weights, minimum match, search mode, vector candidate pool, content quality boost) → optional per-call overrides via tuning:

tuning={"search_mode": "keywords_required", "fw_title": 0.2,
        "mm": "strict", "vector_topk": 500, "quality_boost": 0.3}

Defaults match the platform's PHP configuration exactly — customize in the Control Panel once, or per call from code.

How it's tested

Every release is validated against live Opensolr infrastructure — no mocks:

• Unit tests (offline): location aliases, filter→fq mapping, query building, escaping.
• End-to-end suite: the full write path through the async Data Ingestion queue (queued → server-side enrichment → searchable), semantic / hybrid / lexical retrieval, metadata round-trip, filters, id round-trip (your ids and the Solr md5(uri) ids), deletes by id and by query.
• Real-corpus validation: searches run against a 340-document replica of opensolr.com's own production search index. Verified: pure-semantic hits with zero keyword overlap ("how do I get my data back after a disaster" → backup & restore docs), cross-lingual queries (Romanian query → English content), exact-term surfacing in hybrid mode, all four hybrid modes, and the full alpha range 0 → 1.
• PDF ingestion: a real PDF ingested via rtf:true — server-side text extraction (13k+ chars), automatic content-type detection, then retrieved with a purely semantic query against its contents.

The tools are exercised live (search modes, ingestion with wait, status, deletes, RAG answers) before every release. RAG grounding is verified end-to-end: a question answerable only from the ingested PDF returns the correct answer sourced from the PDF's extracted text.

MIT license.