Can my AI agent restart failed builds for a specific branch?

Yes. Ask the agent to find failed builds across your pipeline by using the list builds tool. Once it locates the specific build number, it can run the rebuild tool instantly, eliminating the need to search through hundreds of logs on the dashboard.

How can I check the status of my physical runner agents?

Ask your agent to list all agents connected to the Buildkite organization. It returns their UUIDs, operating systems, and connection state. If a runner hangs offline, your AI can immediately flag it to the Platform team, saving crucial deployment time.

If a commit is pushed to 'main', can the agent trigger a fresh pipeline deployment?

Absolutely. You can provide the commit SHA (or simply ask it to target 'HEAD' on the 'main' branch) and ask the agent to create a new build. It will hit the Buildkite trigger endpoint with a message of your choosing.

Buildkite MCP Server for LlamaIndex 11 tools — connect in under 2 minutes

Name: Buildkite
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 11 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Buildkite 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 Buildkite. "
            "You have 11 tools available."
        ),
    )

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

asyncio.run(main())

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* 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 Buildkite MCP Server

Connect your Buildkite account to any AI agent and take full control of your CI/CD workflows through natural conversation.

LlamaIndex agents combine Buildkite tool responses with indexed documents for comprehensive, grounded answers. Connect 11 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

Pipelines & Builds — List active pipelines, trigger new builds, retry failed executions, or cancel stuck pipelines instantly
Deep Log Inspection — Retrieve the exact details of specific builds, examining job lists, statuses, and tracking links
Agent Management — Ping your connected build agents globally and verify their status
Organizational Overview — Monitor your organization's scale, retrieve active pipelines across the entire company, and get recent builds map

The Buildkite MCP Server exposes 11 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 Buildkite to LlamaIndex via MCP

Follow these steps to integrate the Buildkite 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 11 tools from Buildkite

Why Use LlamaIndex with the Buildkite MCP Server

LlamaIndex provides unique advantages when paired with Buildkite through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Buildkite tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Buildkite tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Buildkite, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Buildkite tools were called, what data was returned, and how it influenced the final answer

Buildkite + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Buildkite MCP Server delivers measurable value.

Hybrid search: combine Buildkite real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Buildkite 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 Buildkite for fresh data

Analytical workflows: chain Buildkite queries with LlamaIndex's data connectors to build multi-source analytical reports

Buildkite MCP Tools for LlamaIndex (11)

These 11 tools become available when you connect Buildkite to LlamaIndex via MCP:

cancel_build

Cancel a running build

create_build

Trigger a new build for a pipeline

get_access_token_info

Retrieve information about the current API token

get_build

Get details of a specific build

get_pipeline

Get details of a specific pipeline

list_agents

List all build agents in the organization

list_all_builds

List all builds across the entire organization

list_organizations

List all organizations the token has access to

list_pipeline_builds

List builds for a specific pipeline

list_pipelines

List all pipelines in the organization

rebuild

Rebuild a specific build

Example Prompts for Buildkite in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Buildkite immediately.

"What recent builds ran on our production pipeline?"

"Cancel build #205 because of a wrong commit."

"Trigger a new build on HEAD of the main branch for our react-app."

Troubleshooting Buildkite MCP Server with LlamaIndex

Common issues when connecting Buildkite to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Buildkite + LlamaIndex FAQ

Common questions about integrating Buildkite 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 Buildkite tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.