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Vinkius

Chroma (Vector DB) Connector for AI agents.

7 live capabilities

Search and manage your vector embeddings using natural language.

Live agent request Chroma (Vector DB) / Connector

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AI Agent

Why people use Chroma (Vector DB)

Fix Vector Database Management with Chroma (Vector DB)

This Connector changes that by letting you stay inside your AI client. You can just ask your agent to tell you how many documents are in a collection or show you a few examples of the data. It turns a multi-step manual check into a single conversation.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get a conversational interface for your vector database without writing any extra code.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

  1. Real-world use case 01

    Verifying data ingestion

    A developer wants to see if a new batch of documents was successfully indexed by using `count_documents`.

  2. Real-world use case 02

    Auditing production metadata

    A data engineer needs to check the metadata consistency across three different production tenants using `get_collection`.

  3. Real-world use case 03

    Inspecting AI context

    A product manager wants to see what specific documents the AI is remembering for a specific user by using `peek_documents`.

Complete set · 7capabilities

The complete Chroma (Vector DB) capability set.

These are the exact actions your AI can choose when you ask it to work with Chroma (Vector DB).

Capability set01 / 02

01—04

4 capabilities in this set.

Part of 7 available through Chroma (Vector DB).

  1. 01 Capability

    List collections

    See every vector collection in a specific tenant database. This helps you get a clear picture of your data organization at a glance.

  2. 02 Capability

    Get collection

    View the logical settings and configurations for a specific collection. You can use this to check how your data is being bounded or grouped.

  3. 03 Capability

    Count documents

    Get the total number of documents currently in a collection. It's the fastest way to check if your data ingestion scripts finished correctly.

  4. 04 Capability

    Get documents

    Get the exact physical documents and semantic context from a collection. This lets you see the raw data your agent is actually using for answers.

Capability set02 / 02

05—07

3 capabilities in this set.

Part of 7 available through Chroma (Vector DB).

  1. 05 Capability

    Query embeddings

    Find documents that match specific high-dimensional semantic clusters. Use this to test how well your semantic search handles different types of queries.

  2. 06 Capability

    Peek documents

    Get a bounded preview of the database limits and content. It's great for a quick look at the data without pulling the entire collection.

  3. 07 Capability

    Check heartbeat

    Check if the Chroma API nodes are online and reachable. Use this to make sure your connection is active before starting a heavy task.

Set up in minutes

One URL. Then ask Chroma (Vector DB) to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Chroma (Vector DB) from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_aUtliGRmHCVn8DepMSRkGx8uw3Df2TS9KO1N5DG4/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it Chroma (Vector DB), and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable Chroma (Vector DB) for the conversation.

Where the request belongs

Work Chroma can move forward.

Built around the request

This is for the AI developer who is tired of writing boilerplate Python scripts to check their data, the data engineer auditing production volumes, and the product manager who needs to see what context the AI is actually using.

01

AI Developer

Debugging vector search logic and testing RAG pipelines using natural language.

02

Data Engineer

Auditing collection volumes and metadata consistency across different environments.

03

Product Manager

Inspecting the context being fed to AI agents by peeking at stored embeddings.

04

DevOps Engineer

Monitoring instance connectivity and heartbeats for self-hosted Chroma nodes.

When one Connector is not enough

Combine Chroma with the systems that finish the task.

View all recipes
01

Connectors for AI-Powered Trend Detection

By the time a trend reaches your Twitter feed it is too late to act , Tavily detects signals from primary sources, Chroma builds a semantic map that reveals connections between weak signals, and Notion tracks emerging trends weeks before they go mainstream

 02

Connectors to Build AI Training Datasets

You need a dataset of 10,000 product listings for your RAG system but there is no API , Apify scrapes them, Chroma stores them as searchable embeddings, and Notion tracks every data source with quality scores

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

Browse Connectors
OpenSearch Vector logo
01 6 capabilities

OpenSearch Vector

Run k-NN vector searches on OpenSearch. create indexes, upsert embeddings, query similar documents, and manage your vector store from any AI agent.

View Connector
Qdrant logo
02 7 capabilities

Qdrant

Empower your AI to interact directly with your Qdrant vector database. query clusters, perform similarity searches, and manage collections effortlessly.

View Connector
Zilliz Cloud logo
03 10 capabilities

Zilliz Cloud

Manage vector collections and perform similarity searches via Zilliz Cloud.

View Connector
Pinecone logo
04 7 capabilities

Pinecone

Equip your AI agent to manage your Pinecone vector databases. Query embeddings, fetch metrics, manage collections, and run stats natively via chat.

View Connector
Typesense Vector Search logo
05 6 capabilities

Typesense Vector Search

Automate vector similarity searches via Typesense. index documents, manage collections, and execute semantic queries directly from your AI agent.

View Connector
Supabase Vector logo
06 7 capabilities

Supabase Vector

Connect your AI to Supabase Vector. Execute pgvector semantic searches, manage embeddings, and run relational database queries directly from your terminal.

View Connector

Bring your own AI

Change the model, client or framework. Keep Chroma connected.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • VS Code
  • Windsurf
  • ZCode
  • Cline
  • Zed
  • Continue
  • Kiro
  • Roo Code
  • Zencoder
  • Goose
  • Void
  • Augment Code
  • Amp
  • Qodo
  • Tabnine
  • Pieces
  • Sourcegraph Cody
  • JetBrains
  • Warp
  • Amazon Q
  • Antigravity
  • BoltAI
  • Raycast
  • Jan
  • LM Studio
  • AnythingLLM
  • Open WebUI
  • Msty
  • Cherry Studio
  • LibreChat
  • TypingMind
  • Chorus
  • 5ire
  • n8n
  • LangChain
  • LlamaIndex
  • CrewAI
  • Vercel AI SDK

Before you connect

Questions about Chroma.

The practical details behind the request, access and result.

Can I use Chroma (Vector DB) MCP with my own self-hosted instance?

Yes. You can connect to any Chroma instance, whether it's hosted in the cloud or on your own local hardware, just by providing your URL and API key.

How do I check if my embeddings are actually in the database?

You can use the Connector to query specific collections and see the document counts or peek at the content to confirm your data was indexed correctly.

Can I see the metadata for my vector collections?

Yes. The Connector allows your agent to inspect the logical settings and configurations for any specific collection in your database.

Does Chroma (Vector DB) MCP work with Chroma Cloud?

It works perfectly with Chroma Cloud. You just need to provide your cloud URL and API key to get started.

How do I switch between different database tenants?

You can ask your agent to switch between different tenants or databases on the fly to isolate your production and staging environments.

Can I use this to find specific documents by meaning?

Yes. You can ask your agent to perform a semantic search to find documents that match the specific meaning or context of your query.

How do I check if my database is online?

You can simply ask your agent to check the heartbeat, and it will confirm if your Chroma API nodes are reachable and operational.

Can my agent perform semantic search across my collections?

Yes. Provide the vector embedding array in JSON format, and your agent will return the closest document matches along with their distance metrics. It is the perfect way to test your RAG (Retrieval-Augmented Generation) logic without complex scripts.

How can I verify the health of my self-hosted Chroma instance?

Simply ask your agent to check the heartbeat. The agent performs a nanosecond-level responsiveness test against your API nodes, confirming the physical database is active and reachable from the gateway.

I manage multiple tenants. how do I switch between them?

You can define the tenant and database names during the setup phase. If you need to switch often, you can update the credentials in the dashboard. The agent uses these values for all collection and document operations to ensure strict isolation.

One connection away

Give your agent a direct line to Chroma.

Connect Chroma once. Keep it beside 5,900+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available