How to Use the Woodpecker CI MCP in LlamaIndex

Q: How do I retrieve build history using Woodpecker CI and LlamaIndex?

You get raw pipeline details via getpipeline, but indexing this output lets you query it semantically. You can ask, 'Show me all failed builds on the checkout branch last quarter,' pulling answers from indexed historical data.

Q: Can LlamaIndex help manage multiple repositories?

Yes. By calling listrepos and indexing each result, you create a map of every repository name, owner, and status. This allows your RAG app to route complex queries correctly.

Q: What is the best way to monitor agent activity with Woodpecker CI and LlamaIndex?

You can use listagents to get a list of agents, but indexing the output from getagent for each one allows you to build detailed profiles that are searchable by function or owner.

Q: Does Woodpecker CI and LlamaIndex support version tracking?

The getversion tool provides server information. Indexing this output means your application always knows the exact server build it's querying against, which is vital for debugging.

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Connect Woodpecker CI MCP to LlamaIndex

Create your Vinkius account to connect Woodpecker CI 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 Woodpecker CI with LlamaIndex

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Building a Knowledge Base of MCP Server Data

Instead of just running commands, your LlamaIndex application indexes results into a searchable knowledge base. For instance, after calling `get_repo` and getting the full repository JSON, that entire record can become part of an index. Later, you can query past sessions by asking, 'What were the permissions for repo X?' The answer comes grounded in actual API data, not just a generalized guess.

Tracking Changes Using LlamaIndex and MCP Server

You need to know *why* something changed. By using `get_pipeline_config` before an update, you index the original config details. If a user later calls `update_repo`, your RAG system can compare the newly retrieved data against the indexed historical state. This allows you to answer questions like, 'What was the build setting for repo X last Tuesday?'—something simple tool calling can't do.

Auditing System Users with LlamaIndex

Use `list_users` (if admin) and `get_user` to gather user identity data. Indexing this information means you build a searchable directory of who has what roles. If compliance requires knowing every person who touched a specific repo, your index provides that historical audit trail by combining the output of `get_user` with timestamps.

Setup guide

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

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Common questions about Woodpecker CI MCP in LlamaIndex

You get raw pipeline details via `get_pipeline`, but indexing this output lets you query it semantically. You can ask, 'Show me all failed builds on the checkout branch last quarter,' pulling answers from indexed historical data.

Yes. By calling `list_repos` and indexing each result, you create a map of every repository name, owner, and status. This allows your RAG app to route complex queries correctly.

You can use `list_agents` to get a list of agents, but indexing the output from `get_agent` for each one allows you to build detailed profiles that are searchable by function or owner.

The `get_version` tool provides server information. Indexing this output means your application always knows the exact server build it's querying against, which is vital for debugging.

This MCP Server handles system configurations, user/agent metadata, repository structures, pipeline logs (after indexing), and global secrets. The key data type is structured context for retrieval.

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