How to Use the Chroma (Vector DB) MCP in Google ADK

Q: How do I configure Chroma (Vector DB) with Google ADK?

Use StreamableHttpServerParameters with your Vinkius endpoint URL. Wrap that in an McpToolset and pass it to your LlmAgent constructor.

Q: Can I restrict which Chroma (Vector DB) tools my Google ADK agent sees?

You apply a toolnames filter when setting up the toolset. This hides administrative functions like getcollection while keeping search active.

Give your Google ADK agents direct access to Chroma via this managed MCP Server. Search embeddings and inspect collections.

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Connect Chroma (Vector DB) MCP to Google ADK

Create your Vinkius account to connect Chroma (Vector DB) to Google ADK 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 Google ADK

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Perplexity

Semantic Clustering for Gemini

Gemini models thrive on massive context windows. Your LlmAgent uses the `query_embeddings` MCP tool to pull precise logical bounds matching high-dimensional semantic clusters. It feeds those results directly into the 1M+ token context. You configure this by passing a `McpToolset` to your agent. The model evaluates the user request and triggers the vector search autonomously.

Inspect Database Health

Enterprise systems require constant validation. Your agent runs `check_heartbeat` to verify the explicit Chroma API nodes are responsive before attempting a query. If the connection is live, it fires `count_documents` to enumerate total document volumes. That stops your Google Cloud pipeline from operating on an empty vector store.

Read Collections via MCP Server

Managing multiple tenant databases gets complicated. Your Google ADK agent executes the `list_collections` and `get_collection` tools to identify bounded logical settings configuring specific vector blocks. Need to see the actual content? The agent executes `peek_documents` or `get_documents` to grab the physical text. Everything routes securely through the Vinkius zero-trust architecture.

Setup guide

Set up Chroma (Vector DB) MCP in Google ADK

Prerequisites

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

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Chroma (Vector DB) tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Chroma (Vector DB)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Chroma (Vector DB) tools via MCP.",
    tools=mcp_tools,
)

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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 Google ADK

Use `StreamableHttpServerParameters` with your Vinkius endpoint URL. Wrap that in an `McpToolset` and pass it to your LlmAgent constructor.

You apply a `tool_names` filter when setting up the toolset. This hides administrative functions like `get_collection` while keeping search active.

The agent runs semantic searches to find relevant document IDs, then pulls the exact physical documents. Gemini processes the massive text arrays natively.

Yes. Vinkius provides a managed HTTP endpoint. You just pass the provided URL into your server parameters.

Your queries hit an ephemeral sandbox. Vinkius routes the semantic vectors and document arrays without persisting them. The data exists only for the duration of the network request.

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