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.

Chroma (Vector DB) MCP Server for OpenAI Agents SDK 7 tools — connect in under 2 minutes

Name: Chroma (Vector DB)
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 7 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Chroma (Vector DB) through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Chroma (Vector DB) Assistant",
            instructions=(
                "You help users interact with Chroma (Vector DB). "
                "You have access to 7 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Chroma (Vector DB)"
        )
        print(result.final_output)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Chroma (Vector DB) MCP Server

Connect your Chroma vector database to any AI agent and take full control of your semantic data through natural conversation.

The OpenAI Agents SDK auto-discovers all 7 tools from Chroma (Vector DB) through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Chroma (Vector DB), another analyzes results, and a third generates reports, all orchestrated through Vinkius.

What you can do

Vector Collections — List all available collections and inspect their deep configuration and metadata
Semantic Search — Perform high-dimensional vector similarity searches to find relevant context for your LLM applications
Document Auditing — Count documents, peek at unstructured data segments, and retrieve specific records by ID
Instance Health — Monitor heartbeats and connectivity across Chroma Cloud or self-hosted instances
Tenant & Database Management — Switch between different tenants and databases to isolate your production and staging environments

The Chroma (Vector DB) MCP Server exposes 7 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Chroma (Vector DB) to OpenAI Agents SDK via MCP

Follow these steps to integrate the Chroma (Vector DB) MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 7 tools from Chroma (Vector DB)

Why Use OpenAI Agents SDK with the Chroma (Vector DB) MCP Server

OpenAI Agents SDK provides unique advantages when paired with Chroma (Vector DB) through the Model Context Protocol.

Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Chroma (Vector DB) + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Chroma (Vector DB) MCP Server delivers measurable value.

Automated workflows: build agents that query Chroma (Vector DB), process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries Chroma (Vector DB), another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Chroma (Vector DB) tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Chroma (Vector DB) to resolve tickets, look up records, and update statuses without human intervention

Chroma (Vector DB) MCP Tools for OpenAI Agents SDK (7)

These 7 tools become available when you connect Chroma (Vector DB) to OpenAI Agents SDK via MCP:

check_heartbeat

Validate fundamental network availability against explicit Chroma API nodes

count_documents

Execute explicit structural tracking enumerating total document volumes

get_collection

Identify bounded logical settings configuring a specific Vector Collection block

get_documents

Retrieve exact physical documents and semantic context inside known arrays

list_collections

List all explicitly defined Vector Collections within a given tenant database

peek_documents

Extracts explicitly attached bounded preview of the Database limits

query_embeddings

Identify precise logical bounds matching high-dimensional semantic clustering

Example Prompts for Chroma (Vector DB) in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Chroma (Vector DB) immediately.

"List all vector collections"

"Peek at the first 5 documents in 'knowledge-base'"

"Is the Chroma server alive?"

Troubleshooting Chroma (Vector DB) MCP Server with OpenAI Agents SDK

Common issues when connecting Chroma (Vector DB) to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Chroma (Vector DB) + OpenAI Agents SDK FAQ

Common questions about integrating Chroma (Vector DB) MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.