How to Use the Chroma (Vector DB) MCP in Mastra AI

Q: How do I list my Chroma (Vector DB) collections in Mastra AI?

Call listcollections as a setup task in your agent's initial phase. This helps the agent understand which data context to target for the upcoming workflow.

Build complex, failure-aware agent workflows in Mastra AI using direct vector database control.

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Connect Chroma (Vector DB) MCP to Mastra AI

Create your Vinkius account to connect Chroma (Vector DB) to Mastra AI 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 Mastra AI

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Claude

Perplexity

Automated Workflows for Mastra AI

Plug this MCP Server into your Mastra agent to handle data retrieval. Use `query_embeddings` to inform your branching logic based on semantic document matches. If the first query doesn't yield a result, your agent can trigger a retry. This keeps your multi-step processes moving even when data is sparse.

Precise Document Retrieval for Mastra AI

Fetch specific context for your agents using `get_documents`. It pulls exactly what the agent needs to make a decision or write a response. This precision prevents hallucinations. Your agents only reference the data you explicitly define in your collections.

Database Auditing in Mastra AI

Keep your agent workflows honest by checking the state of your vector store. Use `check_heartbeat` to confirm the database is reachable before running a long sequence of tasks. It prevents your agents from failing halfway through a job. You get a clear signal if the connection drops before the work begins.

Setup guide

Set up Chroma (Vector DB) MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Chroma (Vector DB) tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "chroma-vector-db-mcp-client",
  servers: {
    "chroma-vector-db-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Chroma (Vector DB) Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Chroma (Vector DB) tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Chroma (Vector DB) transactions"
);
console.log(result.text);

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Common questions about Chroma (Vector DB) MCP in Mastra AI

You register the server in your agent configuration. The framework then exposes the tools as functions that your agent calls during execution.

Absolutely. Configure your agent to require tool approval for `query_embeddings` or any other sensitive action, ensuring a human reviews the query before it hits the database.

The server supports standard error handling. If a network blip occurs, your agent workflow can catch the exception and automatically attempt the operation again.

Call `list_collections` as a setup task in your agent's initial phase. This helps the agent understand which data context to target for the upcoming workflow.

The server treats your vector metadata as read-only during search. No changes are written back to your database, ensuring your source data remains untouched during agent runs.

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