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 Pydantic AI 7 tools — connect in under 2 minutes

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

ai.pydantic.dev/mcp

Built by Vinkius GDPR 7 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Chroma (Vector DB) through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

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

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Chroma (Vector DB) "
            "(7 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Chroma (Vector DB)?"
    )
    print(result.data)

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.

Pydantic AI validates every Chroma (Vector DB) tool response against typed schemas, catching data inconsistencies at build time. Connect 7 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Chroma (Vector DB) MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 7 tools from Chroma (Vector DB) with type-safe schemas

Why Use Pydantic AI with the Chroma (Vector DB) MCP Server

Pydantic AI provides unique advantages when paired with Chroma (Vector DB) through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Chroma (Vector DB) integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Chroma (Vector DB) connection logic from agent behavior for testable, maintainable code

Chroma (Vector DB) + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Chroma (Vector DB) MCP Server delivers measurable value.

Type-safe data pipelines: query Chroma (Vector DB) with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Chroma (Vector DB) tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Chroma (Vector DB) and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Chroma (Vector DB) responses and write comprehensive agent tests

Chroma (Vector DB) MCP Tools for Pydantic AI (7)

These 7 tools become available when you connect Chroma (Vector DB) to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

Common issues when connecting Chroma (Vector DB) to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Chroma (Vector DB) + Pydantic AI FAQ

Common questions about integrating Chroma (Vector DB) MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Chroma (Vector DB) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.