How to Use the Chroma (Vector DB) MCP in OpenAI Agents SDK

Q: How do I connect Chroma (Vector DB) to the OpenAI Agents SDK?

Install openai-agents via pip. Create an MCPServerStreamableHttp instance using your Vinkius URL and pass it to your Agent constructor. The SDK auto-discovers the tools immediately.

Q: Can the agent check if the database is offline?

Yes. The checkheartbeat tool validates network availability against explicit Chroma API nodes. Your agent knows immediately if the connection drops.

Q: Does Chroma (Vector DB) work with OpenAI Agents SDK caching?

You set cacheToolsList=True when initializing the MCP connection. That stops the agent from fetching the schema on every single run.

Q: How do I preview data in Chroma (Vector DB) using the OpenAI Agents SDK?

Call the peekdocuments tool. It extracts a bounded preview of the database limits so your agent inspects the structure before running heavy embedding queries.

Connect Chroma to your OpenAI Agents SDK system. Run vector searches and manage collections through a secure MCP endpoint.

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Connect Chroma (Vector DB) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Chroma (Vector DB) to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Chroma (Vector DB) with OpenAI Agents SDK

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Perplexity

Semantic Search via OpenAI Agents SDK

Your production agent needs memory. Give your OpenAI agent the `query_embeddings` tool to find high-dimensional semantic matches across your database. The model evaluates user prompts and decides exactly when to pull context. Built-in handoffs mean a specialized retrieval agent handles the search before passing results back to the main router. You track every vector match and tool invocation directly in the OpenAI dashboard.

Audit and Verify Collections

Blindly trusting a vector store leads to hallucinations. Expose the `list_collections` and `get_collection` tools so your agent verifies logical boundaries before executing searches. It sees exactly which tenant databases exist. If a collection looks stale, the agent fires `count_documents`. You set the guardrails in your Python code to prevent the agent from querying empty or misconfigured collections.

Direct Document Retrieval

Sometimes you need exact physical documents instead of semantic clustering. The `get_documents` tool grabs specific arrays of text based on known IDs, completely bypassing fuzzy search. Vinkius runs this MCP Server in an isolated environment. You pass the endpoint URL to `MCPServerStreamableHttp`, and your agent fetches the raw text securely without exposing your internal infrastructure.

Setup guide

Set up Chroma (Vector DB) MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Chroma (Vector DB) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Chroma (Vector DB) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Chroma (Vector DB) tools and returns structured results. Copy the full example on the right to get started.

agent.py

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

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Chroma (Vector DB) Agent",
            instructions="You have access to Chroma (Vector DB) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Chroma. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Chroma (Vector DB) MCP in OpenAI Agents SDK

Install `openai-agents` via pip. Create an `MCPServerStreamableHttp` instance using your Vinkius URL and pass it to your Agent constructor. The SDK auto-discovers the tools immediately.

Yes. The `check_heartbeat` tool validates network availability against explicit Chroma API nodes. Your agent knows immediately if the connection drops.

You set `cacheToolsList=True` when initializing the MCP connection. That stops the agent from fetching the schema on every single run.

Call the `peek_documents` tool. It extracts a bounded preview of the database limits so your agent inspects the structure before running heavy embedding queries.

We process your embeddings and document text inside an ephemeral V8 Isolate Sandbox. The container dies the second your request finishes. Vinkius never stores your high-dimensional arrays.

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