CircleCI MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add CircleCI as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 CircleCI. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in CircleCI?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About CircleCI MCP Server
Connect your CircleCI account to any AI agent and take full control of your CI/CD pipelines and software delivery through natural conversation. Streamline how you monitor and trigger automated builds.
LlamaIndex agents combine CircleCI tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through the 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
- Pipeline Oversight — List and retrieve details for recent CI/CD pipelines across your organizations natively
- Trigger Management — Manually trigger new pipeline runs for specific projects and branches flawlessly
- Workflow Intelligence — Access detailed information for workflows and their constituent jobs securely
- Job Auditing — Retrieve detailed metadata and execution status for specific jobs flawlessly
- Context Logistics — List shared environment contexts used for securing sensitive project data flawlessly
- Developer Insights — Retrieve your own user profile and organization membership information directly within your workspace
The CircleCI MCP Server exposes 8 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 CircleCI to LlamaIndex via MCP
Follow these steps to integrate the CircleCI 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 8 tools from CircleCI
Why Use LlamaIndex with the CircleCI MCP Server
LlamaIndex provides unique advantages when paired with CircleCI through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine CircleCI tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain CircleCI tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query CircleCI, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what CircleCI tools were called, what data was returned, and how it influenced the final answer
CircleCI + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the CircleCI MCP Server delivers measurable value.
Hybrid search: combine CircleCI real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query CircleCI 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 CircleCI for fresh data
Analytical workflows: chain CircleCI queries with LlamaIndex's data connectors to build multi-source analytical reports
CircleCI MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect CircleCI to LlamaIndex via MCP:
get_job_details
Get detailed information for a specific job
get_my_cci_profile
Retrieve information about the authenticated user
get_workflow_details
Get detailed information for a specific workflow
list_cci_contexts
List shared contexts for an organization
list_cci_pipelines
List recent CI/CD pipelines
list_pipeline_workflows
List all workflows within a specific pipeline
list_workflow_jobs
List all jobs within a specific workflow
trigger_cci_pipeline
Trigger a new pipeline for a project
Example Prompts for CircleCI in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with CircleCI immediately.
"List my last 5 pipelines in CircleCI."
"Trigger a new pipeline for project 'gh/acme/api' on the 'main' branch."
"Show me the status of all jobs in workflow ID 'wf-12345'."
Troubleshooting CircleCI MCP Server with LlamaIndex
Common issues when connecting CircleCI to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCircleCI + LlamaIndex FAQ
Common questions about integrating CircleCI MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect CircleCI with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect CircleCI to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.