How to Use the Azure DevOps MCP in LlamaIndex

Q: How do I make Azure DevOps tools available to a LlamaIndex agent?

First, connect with BasicMCPClient. Then, wrap it with McpToolSpec and call totoollistasync(). This gives you a list of tools you can pass directly to your FunctionAgent.

Q: Can I filter which Azure DevOps tools my LlamaIndex agent can use?

Yes, the McpToolSpec supports an allowedtools filter. You can give an agent access to only listworkitems and nothing else, for example. This is good for building specialized, single-purpose bots.

Turn your Azure DevOps activity into a searchable knowledge base your LlamaIndex RAG app can query for grounded answers.

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Connect Azure DevOps MCP to LlamaIndex

Create your Vinkius account to connect Azure DevOps 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 DevOps with LlamaIndex

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Index Your DevOps History

LlamaIndex doesn't just call a tool once; it can systematically index the output. Run `list_work_items`, `list_builds`, and `list_repositories` to pull your project's history into a vector store. Now you have a knowledge base grounded in reality. When you ask "what did we work on last quarter?", the agent queries the indexed data from Azure DevOps, not the open internet.

Build a Grounded Q&A Bot with LlamaIndex

Use this MCP Server to create a RAG pipeline that knows your team's work. The agent can use `list_project_teams` and `list_work_items` to answer questions like "Who is working on the login feature?". The answers are backed by citations from the actual tool calls. This stops hallucinations. Your LlamaIndex app will tell you *why* it thinks a certain team is responsible, pointing back to the data it fetched.

Query Your CI/CD Configuration

You can build an index of all your CI/CD pipelines. An agent can call `list_pipelines` for every project and store the configurations. This lets you ask complex questions in plain English. "Show me all pipelines that deploy to production" becomes a simple query against your LlamaIndex knowledge base, which is kept fresh with data from your Azure DevOps MCP.

Setup guide

Set up Azure DevOps 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 DevOps 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 DevOps tools.",
)
response = await agent.run("List recent Azure DevOps data")

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Common questions about Azure DevOps MCP in LlamaIndex

First, connect with `BasicMCPClient`. Then, wrap it with `McpToolSpec` and call `to_tool_list_async()`. This gives you a list of tools you can pass directly to your `FunctionAgent`.

Yes. You'd set up an ingestion pipeline that periodically calls `list_builds` from this Azure DevOps server and adds the results to a vector index. Your query engine can then search that indexed history to answer questions with LlamaIndex.

Yes. The connection is authenticated through your Vinkius token, which you configure with the necessary permissions. The MCP Server accesses Azure DevOps on your behalf with the rights you've granted.

Yes, the `McpToolSpec` supports an `allowed_tools` filter. You can give an agent access to only `list_work_items` and nothing else, for example. This is good for building specialized, single-purpose bots.

The server only reads metadata from your Azure DevOps organization—things like project names, build statuses, and work item titles. It doesn't access file contents or secrets. Each request is handled in an isolated sandbox and nothing is stored after the call completes.

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