How to Use the CircleCI MCP in LlamaIndex

Turn your CircleCI build history into a searchable knowledge base using LlamaIndex RAG patterns.

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Connect CircleCI MCP to LlamaIndex

Create your Vinkius account to connect CircleCI 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 CircleCI with LlamaIndex

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Index pipeline history in LlamaIndex

Use `list_cci_pipelines` to feed your vector store with historical build data. LlamaIndex then indexes these results for semantic retrieval. This lets you query your past infrastructure performance. You get answers grounded in real API data rather than vague assumptions.

Query build configurations with LlamaIndex

Pull shared contexts using `list_cci_contexts` and store them in your index. Now your RAG application understands the variables governing your builds. Your agent can compare active configurations against historical norms. It spots drift before it results in a broken deployment.

Ground build insights in actual data

The MCP Server provides raw job data through `get_job_details`. LlamaIndex ingests this to keep your knowledge base current and accurate. Stop relying on outdated documentation. Your RAG app queries the live state of your CI/CD environment directly.

Setup guide

Set up CircleCI 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 CircleCI 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 CircleCI tools.",
)
response = await agent.run("List recent CircleCI 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 CircleCI. 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 CircleCI MCP in LlamaIndex

The server tools act as data sources for your indexing pipeline. LlamaIndex transforms these API responses into vector embeddings for your RAG system.

By using the listing tools, you fetch the history and store it in your vector database. You then search through that data using natural language.

Vinkius manages the secure connection. Your API token is isolated in the sandbox, ensuring that only the authorized LlamaIndex agent accesses your project data.

No, because the data comes directly from the API. The agent uses the MCP Server to pull current status, which LlamaIndex then uses to ground its responses.

It reads pipeline metadata, workflow status, and environment contexts. This includes job names and build outcomes, which are stored in your private, secure vector index.

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