• MATCH
  • MISMATCH
  • EMPTY
  • HUMAN

How to debug RAG retrieval accuracy with your AI

Connect Qdrant to your agent to inspect the actual data returned by your vector database. You'll see if your embeddings are hitting the right targets or missing the mark.

Ask AI about this page

Short answer

How can I check if my RAG retrieval is working?

Your AI performs similarity searches in Qdrant and pulls the associated payloads to show you the raw data. This lets you verify if the retrieved context actually matches your query. You'll see exactly where the math and the meaning diverge.

Debugging outcomes

Where your issues land.

The AI categorizes the results of its investigation into four specific areas.

MATCH

Correct context found

The AI finds the exact data points you expected. You'll see the payload matches your query perfectly.

MISMATCH

Irrelevant data returned

The AI identifies points that are mathematically close but contextually wrong. This points to an embedding issue.

EMPTY

No relevant vectors

The AI finds nothing that meets your similarity threshold. This suggests your index is missing necessary data.

HUMAN

Manual index review

The AI flags cases where the data exists but the math is failing. You'll need to step in to adjust your chunking strategy.

The workflow

What your AI does when <the thing arrives>.

The AI uses specific Qdrant operations to pull and inspect your vector data.

  1. Find similar vectors

    The AI executes a similarity search to find the closest matches to your input query.

    search

  2. Inspect point data

    The AI pulls specific points to look at the metadata and payload attached to them.

    get_points

  3. Navigate the index

    The AI moves through your collection to find specific segments of data.

    scroll

  4. Verify payload content

    The AI pulls the raw data from specific points to check if the text matches your query.

    get_points

Try it

Copy these to start.

Use these prompts to begin your debugging session.

Starting points

Swap out the collection names and query strings with your own specific data.

Qdrant Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_q8RmctE5Ixu7FdouvzysiVBMHUlpWlDBAdtwVmus/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Qdrant capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "qdrant-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_q8RmctE5Ixu7FdouvzysiVBMHUlpWlDBAdtwVmus/mcp"
    }
  }
}
Copy into chat04

  • Search the 'customer_support' collection for queries about 'refund policy' and show me the payloads.

  • Find the top 5 most similar points to 'how do I reset my password' in my vector index.

  • Scroll through the 'product_docs' collection and get the payloads for the first 10 points.

  • Check the payload for point ID 5502 to see if the text matches my expected context.

  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Start here

Connect Qdrant once, then ask.

Just link your Qdrant instance. Your credentials stay encrypted, and you can start debugging immediately.

Connect Qdrant to your AI

FAQ

How this task behaves.

  • 01

    Can the AI change my vector embeddings?

    No. The AI can only read and search existing data using the provided tools.

  • 02

    Can the AI delete points from my collection?

    No. This MCP is designed for retrieval and inspection only.

  • 03

    Does the AI see my entire database at once?

    No. The AI only accesses the specific points or segments it retrieves through search or scroll operations.

  • 04

    Can I use this to fix my embedding model?

    The AI can identify where the model is failing by showing you the wrong data, but you have to update the model yourself.

  • 05

    Will this work with any vector database?

    No, this specific MCP is built exclusively for Qdrant.

  • More questions about Qdrant? See everything the Qdrant Connector can do

Connect Qdrant to Claude, Cursor, ChatGPT & more