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Woodpecker CI MCP Server for CrewAIGive CrewAI instant access to 34 tools to Activate Repo, Cancel Pipeline, Chown Repo, and more

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Connect your CrewAI agents to Woodpecker CI through Vinkius, pass the Edge URL in the `mcps` parameter and every Woodpecker CI tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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The Woodpecker CI MCP Server for CrewAI is a standout in the Ship It category — giving your AI agent 34 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

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python
from crewai import Agent, Task, Crew

agent = Agent(
    role="Woodpecker CI Specialist",
    goal="Help users interact with Woodpecker CI effectively",
    backstory=(
        "You are an expert at leveraging Woodpecker CI tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Woodpecker CI "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 34 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)
Woodpecker CI
Fully ManagedVinkius Servers
60%Token savings
High SecurityEnterprise-grade
IAMAccess control
EU AI ActCompliant
DLPData protection
V8 IsolateSandboxed
Ed25519Audit chain
<40msKill switch
Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Woodpecker CI MCP Server

Connect your Woodpecker CI server to any AI agent to automate your continuous integration and deployment workflows through natural language.

When paired with CrewAI, Woodpecker CI becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Woodpecker CI tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

  • Pipeline Control — List, trigger, restart, or cancel pipelines for any repository to keep your builds moving.
  • Agent Monitoring — View all connected agents, check their health metrics, and manage task assignments in real-time.
  • Repository Management — Activate new repositories, update settings, and repair webhooks without leaving your chat interface.
  • Secret & Config Management — Securely handle global, organization, or repository-level secrets and inspect pipeline configurations.
  • System Insights — Retrieve server version, health status, and performance metrics to ensure your CI infrastructure is running smoothly.

The Woodpecker CI MCP Server exposes 34 tools through the Vinkius. Connect it to CrewAI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 34 Woodpecker CI tools available for CrewAI

When CrewAI connects to Woodpecker CI through Vinkius, your AI agent gets direct access to every tool listed below — spanning ci-cd, pipelines, automation, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

activate

Activate repo on Woodpecker CI

Activate a repository

cancel

Cancel pipeline on Woodpecker CI

Cancel a running pipeline

chown

Chown repo on Woodpecker CI

Change repository owner to the current user

create

Create agent on Woodpecker CI

Create a new Woodpecker agent

create

Create global secret on Woodpecker CI

Create a global secret

create

Create repo secret on Woodpecker CI

Create a repository secret

delete

Delete agent on Woodpecker CI

Delete an agent

delete

Delete pipeline on Woodpecker CI

Delete a pipeline

delete

Delete repo on Woodpecker CI

Deactivate/delete a repository

get

Get agent on Woodpecker CI

Get details of a specific agent

get

Get healthz on Woodpecker CI

Server health check

get

Get metrics on Woodpecker CI

Prometheus metrics (requires WOODPECKER_PROMETHEUS_AUTH_TOKEN if configured)

get

Get org permissions on Woodpecker CI

Get user permissions for an organization

get

Get pipeline on Woodpecker CI

Get details of a specific pipeline

get

Get pipeline config on Woodpecker CI

Get the configuration files used for a pipeline

get

Get repo on Woodpecker CI

Get repository details

get

Get user on Woodpecker CI

Get the currently authenticated user

get

Get version on Woodpecker CI

Get server version information

list

List agent tasks on Woodpecker CI

List tasks currently assigned to an agent

list

List agents on Woodpecker CI

List all Woodpecker agents

list

List global secrets on Woodpecker CI

List global secrets (Admin only)

list

List org agents on Woodpecker CI

List agents scoped to an organization

list

List org secrets on Woodpecker CI

List organization-level secrets

list

List orgs on Woodpecker CI

List all organizations

list

List pipelines on Woodpecker CI

List pipelines for a repository

list

List repo secrets on Woodpecker CI

List repository-level secrets

list

List repos on Woodpecker CI

List all repositories on the server

list

List users on Woodpecker CI

List all users (Admin only)

lookup

Lookup repo on Woodpecker CI

Lookup a repository by its full name (slug)

repair

Repair repo on Woodpecker CI

Repair repository webhooks

restart

Restart pipeline on Woodpecker CI

Restart a pipeline

trigger

Trigger pipeline on Woodpecker CI

Trigger a manual pipeline

update

Update agent on Woodpecker CI

Update an existing agent

update

Update repo on Woodpecker CI

Update repository settings

Connect Woodpecker CI to CrewAI via MCP

Follow these steps to wire Woodpecker CI into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

01

Install CrewAI

Run pip install crewai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
03

Customize the agent

Adjust the role, goal, and backstory to fit your use case
04

Run the crew

Run python crew.py. CrewAI auto-discovers 34 tools from Woodpecker CI

Why Use CrewAI with the Woodpecker CI MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Woodpecker CI through the Model Context Protocol.

01

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

02

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

03

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

04

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Woodpecker CI + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Woodpecker CI MCP Server delivers measurable value.

01

Automated multi-step research: a reconnaissance agent queries Woodpecker CI for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

02

Scheduled intelligence reports: set up a crew that periodically queries Woodpecker CI, analyzes trends over time, and generates executive briefings in markdown or PDF format

03

Multi-source enrichment pipelines: chain Woodpecker CI tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

04

Compliance and audit automation: a compliance agent queries Woodpecker CI against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Example Prompts for Woodpecker CI in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Woodpecker CI immediately.

01

"List all Woodpecker agents and show their current status."

02

"Find the repository 'vinkius/mcp-server' and trigger a new pipeline."

03

"Show me the last 5 pipelines for repository ID 42."

Troubleshooting Woodpecker CI MCP Server with CrewAI

Common issues when connecting Woodpecker CI to CrewAI through Vinkius, and how to resolve them.

01

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.
02

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".
03

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.
04

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Woodpecker CI + CrewAI FAQ

Common questions about integrating Woodpecker CI MCP Server with CrewAI.

01

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.
02

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.
03

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.
04

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.
05

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.

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