# How to integrate Woodpecker co MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Woodpecker co MCP with OpenAI Agents SDK",
  "toolkit": "Woodpecker co",
  "toolkit_slug": "woodpecker_co",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/woodpecker_co/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/woodpecker_co/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-05-12T10:30:29.516Z"
}
```

## Introduction

This guide walks you through connecting Woodpecker co to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Woodpecker co agent that can send a cold email campaign to new leads, pause all follow-ups for selected prospects, get stats for last week's email campaigns through natural language commands.
This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Woodpecker co account through Composio's Woodpecker co MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Woodpecker co with

- [Claude Agent SDK](https://composio.dev/toolkits/woodpecker_co/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/woodpecker_co/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/woodpecker_co/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/woodpecker_co/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/woodpecker_co/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/woodpecker_co/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/woodpecker_co/framework/cli)
- [Google ADK](https://composio.dev/toolkits/woodpecker_co/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/woodpecker_co/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/woodpecker_co/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/woodpecker_co/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/woodpecker_co/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/woodpecker_co/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Get and set up your OpenAI and Composio API keys
- Install the necessary dependencies
- Initialize Composio and create a Tool Router session for Woodpecker co
- Configure an AI agent that can use Woodpecker co as a tool
- Run a live chat session where you can ask the agent to perform Woodpecker co operations

## What is OpenAI Agents SDK?

The OpenAI Agents SDK is a lightweight framework for building AI agents that can use tools and maintain conversation state. It provides a simple interface for creating agents with hosted MCP tool support.
Key features include:
- Hosted MCP Tools: Connect to external services through hosted MCP endpoints
- SQLite Sessions: Persist conversation history across interactions
- Simple API: Clean interface with Agent, Runner, and tool configuration
- Streaming Support: Real-time response streaming for interactive applications

## What is the Woodpecker co MCP server, and what's possible with it?

The Woodpecker co MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Woodpecker co account. It provides structured and secure access to your outreach campaigns, prospects, and follow-up sequences, so your agent can perform actions like managing email campaigns, scheduling automated follow-ups, analyzing performance, and updating prospect lists on your behalf.
- Automated campaign management: Launch, pause, or monitor cold email campaigns directly from your agent, making it easy to scale outreach without manual intervention.
- Personalized follow-up scheduling: Let your agent set up and adjust automated follow-ups for prospects, ensuring timely and consistent communication.
- Prospect and contact list updates: Add, modify, or segment contacts so your outreach stays accurate and targeted at the right leads.
- Performance analytics and reporting: Ask your agent to fetch open rates, response statistics, and campaign results to keep your team in the loop.
- Bounce and reply handling: Handle bounced emails, auto-replies, and unsubscribe requests automatically, so your campaigns stay clean and compliant.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `WOODPECKER_CO_ADD_PROSPECTS_TO_CAMPAIGN_V1` | Add prospects to a campaign (v1) | Tool to add one or multiple prospects to a campaign. Use when you need to import prospects into a campaign's contact list. |
| `WOODPECKER_CO_DELETE_CAMPAIGN_STEP` | Delete Campaign Step | Tool to delete a non-initial campaign step that hasn't processed any prospects. Use when the campaign is in DRAFT or EDITED status and the step is unused. |
| `WOODPECKER_CO_GET_PROSPECTS_IN_CAMPAIGNS_V1` | Get Prospects in Campaigns (v1) | Tool to retrieve prospects enrolled in specified campaigns. Use when you need to list prospects for given campaign IDs. |
| `WOODPECKER_CO_LIST_AVAILABLE_WEBHOOK_EVENTS` | List Available Webhook Events | Tool to list all webhook event names supported by Woodpecker. Use before subscribing to ensure valid 'event' values (static catalog from docs). |
| `WOODPECKER_CO_LIST_CAMPAIGNS_V1` | List Campaigns V1 | Tool to list campaigns. Use when you need to fetch campaigns with optional status or ID filters. |
| `WOODPECKER_CO_PAUSE_CAMPAIGN` | Pause Campaign | Tool to pause a campaign. Use when you need to temporarily stop sending prospects until resumed. |
| `WOODPECKER_CO_RUN_CAMPAIGN` | Run Campaign | Tool to run a campaign and set its status to RUNNING. Use when you want to activate a configured campaign after final review. |
| `WOODPECKER_CO_STOP_CAMPAIGN` | Stop Campaign | Tool to stop a campaign. Use when you need to halt prospect contacts by setting campaign status to STOPPED. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Woodpecker co MCP server is an implementation of the Model Context Protocol that connects your AI agent to Woodpecker co. It provides structured and secure access so your agent can perform Woodpecker co operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

Before starting, make sure you have:
- Composio API Key and OpenAI API Key
- Primary know-how of OpenAI Agents SDK
- A live Woodpecker co project
- Some knowledge of Python or Typescript

### 1. Getting API Keys for OpenAI and Composio

OpenAI API Key
- Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider.
- Keep the API key safe.
Composio API Key
- Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs).
- Go to Settings and copy your API key.

### 2. Install dependencies

Install the Composio SDK and the OpenAI Agents SDK.
```python
pip install composio_openai_agents openai-agents python-dotenv
```

```typescript
npm install @composio/openai-agents @openai/agents dotenv
```

### 3. Set up environment variables

Create a .env file and add your OpenAI and Composio API keys.
```bash
OPENAI_API_KEY=sk-...your-api-key
COMPOSIO_API_KEY=your-api-key
USER_ID=composio_user@gmail.com
```

### 4. Import dependencies

What's happening:
- You're importing all necessary libraries.
- The Composio and OpenAIAgentsProvider classes are imported to connect your OpenAI agent to Composio tools like Woodpecker co.
```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';
```

### 5. Set up the Composio instance

No description provided.
```python
load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())
```

```typescript
dotenv.config();

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});
```

### 6. Create a Tool Router session

What is happening:
- You give the Tool Router the user id and the toolkits you want available. Here, it is only woodpecker_co.
- The router checks the user's Woodpecker co connection and prepares the MCP endpoint.
- The returned session.mcp.url is the MCP URL that your agent will use to access Woodpecker co.
- This approach keeps things lightweight and lets the agent request Woodpecker co tools only when needed during the conversation.
```python
# Create a Woodpecker co Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["woodpecker_co"]
)

mcp_url = session.mcp.url
```

```typescript
// Create Tool Router session for Woodpecker co
const session = await composio.create(userId as string, {
  toolkits: ['woodpecker_co'],
});
const mcpUrl = session.mcp.url;
```

### 7. Configure the agent

No description provided.
```python
# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Woodpecker co. "
        "Help users perform Woodpecker co operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)
```

```typescript
// Configure agent with MCP tool
const agent = new Agent({
  name: 'Assistant',
  model: 'gpt-5',
  instructions:
    'You are a helpful assistant that can access Woodpecker co. Help users perform Woodpecker co operations through natural language.',
  tools: [
    hostedMcpTool({
      serverLabel: 'tool_router',
      serverUrl: mcpUrl,
      headers: { 'x-api-key': composioApiKey },
      requireApproval: 'never',
    }),
  ],
});
```

### 8. Start chat loop and handle conversation

No description provided.
```python
print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
// Keep conversation state across turns
const conversationSession = new OpenAIConversationsSession();

// Simple CLI
const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
  prompt: 'You: ',
});

console.log('\nComposio Tool Router session created.');
console.log('\nChat started. Type your requests below.');
console.log("Commands: 'exit', 'quit', or 'q' to end\n");

try {
  const first = await run(agent, 'What can you help me with?', { session: conversationSession });
  console.log(`Assistant: ${first.finalOutput}\n`);
} catch (e) {
  console.error('Error:', e instanceof Error ? e.message : e, '\n');
}

rl.prompt();

rl.on('line', async (userInput) => {
  const text = userInput.trim();

  if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
    console.log('Goodbye!');
    rl.close();
    process.exit(0);
  }

  if (!text) {
    rl.prompt();
    return;
  }

  try {
    const result = await run(agent, text, { session: conversationSession });
    console.log(`\nAssistant: ${result.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();
});

rl.on('close', () => {
  console.log('\n👋 Session ended.');
  process.exit(0);
});
```

## Complete Code

```python
import asyncio
import os
from dotenv import load_dotenv

from composio import Composio
from composio_openai_agents import OpenAIAgentsProvider
from agents import Agent, Runner, HostedMCPTool, SQLiteSession

load_dotenv()

api_key = os.getenv("COMPOSIO_API_KEY")
user_id = os.getenv("USER_ID")

if not api_key:
    raise RuntimeError("COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key")

# Initialize Composio
composio = Composio(api_key=api_key, provider=OpenAIAgentsProvider())

# Create Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["woodpecker_co"]
)
mcp_url = session.mcp.url

# Configure agent with MCP tool
agent = Agent(
    name="Assistant",
    model="gpt-5",
    instructions=(
        "You are a helpful assistant that can access Woodpecker co. "
        "Help users perform Woodpecker co operations through natural language."
    ),
    tools=[
        HostedMCPTool(
            tool_config={
                "type": "mcp",
                "server_label": "tool_router",
                "server_url": mcp_url,
                "headers": {"x-api-key": api_key},
                "require_approval": "never",
            }
        )
    ],
)

print("\nComposio Tool Router session created.")

chat_session = SQLiteSession("conversation_openai_toolrouter")

print("\nChat started. Type your requests below.")
print("Commands: 'exit', 'quit', or 'q' to end\n")

async def main():
    try:
        result = await Runner.run(
            agent,
            "What can you help me with?",
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")
    except Exception as e:
        print(f"Error: {e}\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "q"}:
            print("Goodbye!")
            break

        result = await Runner.run(
            agent,
            user_input,
            session=chat_session
        )
        print(f"Assistant: {result.final_output}\n")

asyncio.run(main())
```

```typescript
import 'dotenv/config';
import { Composio } from '@composio/core';
import { OpenAIAgentsProvider } from '@composio/openai-agents';
import { Agent, hostedMcpTool, run, OpenAIConversationsSession } from '@openai/agents';
import * as readline from 'readline';

const composioApiKey = process.env.COMPOSIO_API_KEY;
const userId = process.env.USER_ID;

if (!composioApiKey) {
  throw new Error('COMPOSIO_API_KEY is not set. Create a .env file with COMPOSIO_API_KEY=your_key');
}
if (!userId) {
  throw new Error('USER_ID is not set');
}

// Initialize Composio
const composio = new Composio({
  apiKey: composioApiKey,
  provider: new OpenAIAgentsProvider(),
});

async function main() {
  // Create Tool Router session
  const session = await composio.create(userId as string, {
    toolkits: ['woodpecker_co'],
  });
  const mcpUrl = session.mcp.url;

  // Configure agent with MCP tool
  const agent = new Agent({
    name: 'Assistant',
    model: 'gpt-5',
    instructions:
      'You are a helpful assistant that can access Woodpecker co. Help users perform Woodpecker co operations through natural language.',
    tools: [
      hostedMcpTool({
        serverLabel: 'tool_router',
        serverUrl: mcpUrl,
        headers: { 'x-api-key': composioApiKey },
        requireApproval: 'never',
      }),
    ],
  });

  // Keep conversation state across turns
  const conversationSession = new OpenAIConversationsSession();

  // Simple CLI
  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: 'You: ',
  });

  console.log('\nComposio Tool Router session created.');
  console.log('\nChat started. Type your requests below.');
  console.log("Commands: 'exit', 'quit', or 'q' to end\n");

  try {
    const first = await run(agent, 'What can you help me with?', { session: conversationSession });
    console.log(`Assistant: ${first.finalOutput}\n`);
  } catch (e) {
    console.error('Error:', e instanceof Error ? e.message : e, '\n');
  }

  rl.prompt();

  rl.on('line', async (userInput) => {
    const text = userInput.trim();

    if (['exit', 'quit', 'q'].includes(text.toLowerCase())) {
      console.log('Goodbye!');
      rl.close();
      process.exit(0);
    }

    if (!text) {
      rl.prompt();
      return;
    }

    try {
      const result = await run(agent, text, { session: conversationSession });
      console.log(`\nAssistant: ${result.finalOutput}\n`);
    } catch (e) {
      console.error('Error:', e instanceof Error ? e.message : e, '\n');
    }

    rl.prompt();
  });

  rl.on('close', () => {
    console.log('\nSession ended.');
    process.exit(0);
  });
}

main().catch((err) => {
  console.error('Fatal error:', err);
  process.exit(1);
});
```

## Conclusion

This was a starter code for integrating Woodpecker co MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Woodpecker co.
Key features:
- Hosted MCP tool integration through Composio's Tool Router
- SQLite session persistence for conversation history
- Simple async chat loop for interactive testing
You can extend this by adding more toolkits, implementing custom business logic, or building a web interface around the agent.

## How to build Woodpecker co MCP Agent with another framework

- [Claude Agent SDK](https://composio.dev/toolkits/woodpecker_co/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/woodpecker_co/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/woodpecker_co/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/woodpecker_co/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/woodpecker_co/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/woodpecker_co/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/woodpecker_co/framework/cli)
- [Google ADK](https://composio.dev/toolkits/woodpecker_co/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/woodpecker_co/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/woodpecker_co/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/woodpecker_co/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/woodpecker_co/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/woodpecker_co/framework/crew-ai)

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- [Convolo ai](https://composio.dev/toolkits/convolo_ai) - Convolo ai is an AI-powered communications platform for sales teams. It accelerates lead response and improves conversion rates by automating calls and integrating workflows.
- [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment.
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- [Findymail](https://composio.dev/toolkits/findymail) - Findymail is a B2B data provider offering verified email and phone contacts for sales prospecting. Enhance outreach with automated exports, email verification, and CRM enrichment.
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- [Fullenrich](https://composio.dev/toolkits/fullenrich) - FullEnrich is a B2B contact enrichment platform that aggregates emails and phone numbers from 15+ data vendors. Instantly find and verify lead contact data to boost your outreach.
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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Woodpecker co MCP?

With a standalone Woodpecker co MCP server, the agents and LLMs can only access a fixed set of Woodpecker co tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Woodpecker co and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with OpenAI Agents SDK?

Yes, you can. OpenAI Agents SDK fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Woodpecker co tools.

### Can I manage the permissions and scopes for Woodpecker co while using Tool Router?

Yes, absolutely. You can configure which Woodpecker co scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

### How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Woodpecker co data and credentials are handled as safely as possible.

---
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