• VALIDATION
  • METADATA
  • REPAIR
  • HUMAN

How to debug RAG retrieval context with your AI

Connect Chroma to your agent to look under the hood of your vector database. You'll see the exact chunks and metadata feeding your RAG pipeline.

Ask AI about this page

Short answer

How can I check if my RAG pipeline is pulling the right context?

Use your agent to pull specific document chunks and their associated metadata directly from Chroma. This lets you verify if the retrieved context actually contains the information needed to answer your queries.

Where every issue lands.

Debugging outcomes

Your agent uses Chroma tools to isolate and inspect the data causing retrieval errors.

VALIDATION

Verify context accuracy

The agent pulls specific chunks to confirm they match your query intent. You'll see if the vector search is hitting the right targets.

METADATA

Check filtering logic

Inspect the metadata attached to retrieved documents. This helps you see if your filters are working or if they are too restrictive.

REPAIR

Identify missing data

Find out why a query failed by looking at what was actually retrieved. You'll know if the information is missing from the DB entirely.

HUMAN

Manual index cleanup

The AI identifies the problematic chunks. You then decide if you need to re-index, change your chunk size, or update your embedding model.

The workflow

What your AI does when the retrieval fails.

Instead of guessing why an answer is wrong, your agent investigates the database directly.

  1. Find relevant vectors

    The agent searches the vector space to find the closest matches to your specific question.

    query_embeddings

  2. Fetch document content

    Once matches are found, the agent pulls the actual text chunks and metadata for those specific IDs.

    get_documents

  3. Inspect specific chunks

    The agent looks at a single document or a small subset to check for noise or formatting issues.

    peek_documents

  4. Compare and report

    The agent compares the retrieved text against your original query to explain why the context is or isn't sufficient.

    get_documentsquery_embeddings

Try it

Copy these to start.

Use these prompts to begin your debugging session.

Starting points

Replace the bracketed text with your specific collection names or query strings.

Chroma Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_aUtliGRmHCVn8DepMSRkGx8uw3Df2TS9KO1N5DG4/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 — Chroma capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "chroma-vector-db-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_aUtliGRmHCVn8DepMSRkGx8uw3Df2TS9KO1N5DG4/mcp"
    }
  }
}
Copy into chat04

  • Query the 'customer_support' collection for the most relevant chunks regarding 'refund policy'.

  • Peek at the first five documents in the 'product_manual' collection to check metadata.

  • Get the full text and metadata for the documents returned by a search for 'battery life'.

  • Run an embedding query on the 'legal_docs' collection and show me the metadata for the top 3 results.

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

Start here

Connect Chroma once, then ask.

Just link your Chroma instance. Your credentials stay encrypted and you can immediately start debugging with your agent.

Connect Chroma to your AI

FAQ

How this task behaves

  • 01

    Can the AI delete documents from my Chroma collection?

    No. This MCP only provides tools to read and query data. It cannot delete or modify your vector database.

  • 02

    Can I use this to add new embeddings to my database?

    No. The available tools are limited to querying and inspecting existing data. You must use your own pipeline to add new documents.

  • 03

    Does the AI see my entire database at once?

    No. The AI only sees the specific documents or metadata returned by the tools after it performs a query or a peek.

  • 04

    Can the AI fix my embedding model?

    No. The AI can only report on what is currently in the database. It cannot change how your embeddings are generated.

  • 05

    Can I filter by metadata using the agent?

    Yes, if that metadata exists in Chroma, the agent can use the retrieval tools to find those specific documents.

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

Connect Chroma to Claude, Cursor, ChatGPT & more