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 LlamaIndex 7 tools — connect in under 2 minutes

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

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 7 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Chroma (Vector DB) as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Chroma (Vector DB). "
            "You have 7 tools available."
        ),
    )

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

asyncio.run(main())

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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.

LlamaIndex agents combine Chroma (Vector DB) tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex via MCP

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

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

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)

Why Use LlamaIndex with the Chroma (Vector DB) MCP Server

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

Data-first architecture: LlamaIndex agents combine Chroma (Vector DB) tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Chroma (Vector DB) tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Chroma (Vector DB), a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Chroma (Vector DB) tools were called, what data was returned, and how it influenced the final answer

Chroma (Vector DB) + LlamaIndex Use Cases

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

Hybrid search: combine Chroma (Vector DB) real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Chroma (Vector DB) to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Chroma (Vector DB) for fresh data

Analytical workflows: chain Chroma (Vector DB) queries with LlamaIndex's data connectors to build multi-source analytical reports

Chroma (Vector DB) MCP Tools for LlamaIndex (7)

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

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Chroma (Vector DB) + LlamaIndex FAQ

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

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Chroma (Vector DB) tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.