mcp_qrant — Vector-Store MCP Server

A Python/FastAPI MCP server (JSON-RPC 2.0) that exposes a unified interface for storing and querying vector databases. Phase 1 ships Qdrant (self-embedding, local or remote). Nine more providers are listed and will be activated in later phases.

Quick Start

# from the project root
MCPQ_PORT=8008 ./.venv/bin/python server.py

The server listens on http://127.0.0.1:8008 and handles MCP JSON-RPC at both / and /mcp.

Environment variables:

Related MCP server: Qdrant MCP Server

Tools (5)

list_providers

Returns all 10 provider descriptors. Each entry includes available (bool), embeds_internally (bool), and connection_schema (field list for the UI form).

list_embeddings

Returns the embedding-model catalog (FastEmbed BGE, HuggingFace MiniLM, OpenAI small/large). Only relevant for providers where embeds_internally: false — those need a model chosen from this list to embed text before upsert/search.

test_connection

Validates a provider connection without writing data.

{"provider": "qdrant", "connection": {"mode": "local", "path": "/tmp/mydb"}}

Returns {"ok": true} or {"ok": false, "message": "..."}.

store

Embeds (if needed) and upserts text chunks into a collection.

{
  "provider": "qdrant",
  "connection": {"mode": "local", "path": "/tmp/mydb"},
  "collection": "my_docs",
  "items": [
    {"text": "solar panels convert sunlight", "metadata": {"src": "wiki"}},
    {"text": "the cat sat on the mat",       "metadata": {"src": "test"}}
  ]
}

Returns {"stored": 2, "errors": 0}.

embedding is optional; Qdrant ignores it (self-embeds). Pass an id from list_embeddings for future non-self-embedding providers.

find

Semantic search over a collection.

{
  "provider": "qdrant",
  "connection": {"mode": "local", "path": "/tmp/mydb"},
  "collection": "my_docs",
  "query": "renewable energy",
  "limit": 3
}

Returns {"results": [{"text": "...", "metadata": {...}, "score": 0.91}, ...]}.

Providers (10)

embeds_internally

• true (Qdrant): the provider does its own embedding via qdrant-client[fastembed]. Pass text in items[].text and query; the embedding field is ignored.

• false (all others, Phase 2+): the MCP will embed text using the model you select from list_embeddings before upserting or searching. Pass the model id in the embedding field.

Connection Schemas

list_providers returns a connection_schema per provider that drives the UI form. Qdrant's fields:

Running Tests

./.venv/bin/python -m pytest tests/ -v

18 tests, all green (Tasks 1–7).

Smoke Test

cd tests && ./smoke.sh

Boots the server on port 8008, fires list_providers (10 entries), store 2 chunks into a temp local Qdrant path, and find with a semantic query — then shuts down and cleans up.

PHP Prototype (Retired)

An earlier PHP prototype (htdocs/vector/) implemented qdrant-store/qdrant-find as a thin stateless translator to Qdrant Cloud REST. That work informed the tool contract but is superseded by this Python server. The PHP files will be retired at the remote-Qdrant cutover when this MCP handles both local and remote modes end-to-end.

Architecture

server.py          FastAPI app + JSON-RPC router (_wrap envelope)
handlers.py        Handlers class — delegates to registry
stores/
  base.py          VectorStoreProvider protocol
  registry.py      build_registry + providers_payload
  qdrant.py        QdrantProvider (connect/test_connection/store/find)
providers/         10 JSON descriptor files (available, embeds_internally, connection_schema)
embedding.py       Embedding catalog (used by future non-self-embedding providers)
config.py          AppConfig (env-var driven)
tests/
  smoke.sh         Live boot + curl smoke (store/find against real local Qdrant)
  test_*.py        18 unit tests