How to Use the Azure Cosmos DB Container MCP in LlamaIndex

Q: How do I load Azure Cosmos DB Container data into LlamaIndex?

Use the BasicMCPClient to connect to Vinkius. Wrap it in McpToolSpec and pass the resulting tool list to your agent.

Turn live Azure database records into searchable vector indexes using LlamaIndex and this MCP Server.

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Connect Azure Cosmos DB Container MCP to LlamaIndex

Create your Vinkius account to connect Azure Cosmos DB Container to LlamaIndex 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 Azure Cosmos DB Container with LlamaIndex

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Index NoSQL records with LlamaIndex

The `get_document` tool feeds specific Cosmos DB records directly into your LlamaIndex knowledge base. Your RAG application pulls live JSON data instead of relying on stale text dumps. Storing raw database output in a vector store means your agent actually understands the current state of your system. You query past sessions or configurations, and the engine grounds its answers in real API responses rather than guessing.

Query documents for RAG pipelines

Using `query_documents` lets your index fetch exactly the subset of data it needs before generating an answer. The MCP server accepts parameterized SQL statements to filter records by status, date, or user ID. Pass `include_resources=True` during your client setup. This ensures the raw NoSQL responses flow straight into your document chunks for semantic search.

Write back from the MCP Server

Your `FunctionAgent` can trigger `create_document` to store new insights right back into your Azure container. You pass the ID and partition key, and the agent handles the JSON formatting. Removing outdated information is just as fast with `delete_document`. Keeping your database clean ensures your vector index doesn't get polluted with deprecated records over time.

Setup guide

Set up Azure Cosmos DB Container MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Azure Cosmos DB Container MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Azure Cosmos DB Container tools.",
)
response = await agent.run("List recent Azure Cosmos DB Container data")

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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 Azure Cosmos DB Container. 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 Azure Cosmos DB Container MCP in LlamaIndex

Use the `BasicMCPClient` to connect to Vinkius. Wrap it in `McpToolSpec` and pass the resulting tool list to your agent.

The agent writes Cosmos SQL queries to filter data before it ever reaches the vector store. This keeps your index focused on relevant documents.

It works perfectly. The agent decides when to read or write database records based on the user's prompt.

No. The server configuration already targets a single specific container. Your SQL just needs to select from the generic alias.

Only your local client. Vinkius routes your Cosmos DB JSON payloads through an isolated MCP sandbox that forgets everything immediately. We hold no logs of your database contents or query structures.

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