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Qdrant

Vector database for semantic search and RAG applications.

Category: Database
Difficulty: Medium

Quick Configuration

Choose the setup that matches your environment.

Before You Start

  • Confirm you have the account, endpoint, or API key required for Qdrant.
  • Start with minimum scopes and read-only access where possible.
  • Keep secrets in environment variables instead of hardcoding them in JSON.

This MCP does not have a tested config yet

Do not treat a generic endpoint placeholder as a real setup. Verify the official install path, command/url, env vars, and scopes first; if you already have sanitized config, run it through the checker.

Open config checker

Common Pitfalls & Fixes

  • ⚠️ Watch out: API key authentication, collection setup, and vector dimension matching.
  • 🔑 Always store API keys in environment variables, never hardcode them in JSON.
  • 🛡️ Start with read-only scopes if available to verify connection safely.

Example Prompts

Once connected, try these prompts to test capabilities:

  • Search for similar vectors in a collection using a text query.
  • List all collections and their point counts.
  • Add new points to an existing collection.

Verification Checklist

  • Run 1-2 real prompts to confirm Qdrant returns usable data.
  • Check that error messages are clear enough for troubleshooting.
  • Document the required scopes, dependencies, and env vars for future reuse.