# How to integrate Datagma MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Datagma MCP with OpenAI Agents SDK",
  "toolkit": "Datagma",
  "toolkit_slug": "datagma",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/datagma/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/datagma/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-05-12T10:08:20.171Z"
}
```

## Introduction

This guide walks you through connecting Datagma to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Datagma agent that can identify top competitors in your industry, find recent market trends for saas, analyze growth opportunities in fintech through natural language commands.
This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Datagma account through Composio's Datagma MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Datagma with

- [Claude Agent SDK](https://composio.dev/toolkits/datagma/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/datagma/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/datagma/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/datagma/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/datagma/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/datagma/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/datagma/framework/cli)
- [Google ADK](https://composio.dev/toolkits/datagma/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/datagma/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/datagma/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/datagma/framework/mastra-ai)
- [LlamaIndex](https://composio.dev/toolkits/datagma/framework/llama-index)
- [CrewAI](https://composio.dev/toolkits/datagma/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 Datagma
- Configure an AI agent that can use Datagma as a tool
- Run a live chat session where you can ask the agent to perform Datagma 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 Datagma MCP server, and what's possible with it?

The Datagma MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Datagma account. It provides structured and secure access to your Datagma data intelligence platform, so your agent can perform actions like uncovering market insights, tracking competitor activities, analyzing industry trends, and supporting strategic growth decisions on your behalf.
- In-depth market insights extraction: Enable your agent to gather and analyze real-time market data to identify emerging opportunities and potential threats.
- Competitor metrics tracking: Let your agent monitor competitor performance, product launches, and strategic moves for sharper benchmarking.
- Growth opportunity identification: Task your agent with surfacing new business prospects and growth areas using Datagma's data intelligence resources.
- Customized analytics reporting: Have your agent generate tailored reports and dashboards that summarize key metrics and actionable insights.
- Trend and pattern analysis: Empower your agent to spot industry trends, shifts in customer behavior, and evolving market dynamics for proactive strategy planning.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `DATAGMA_DETECT_JOB_CHANGE` | Detect Job Change | Tool to detect if a contact changed jobs. Use when verifying a contact’s current employment details by email. |
| `DATAGMA_ENRICH_PERSON_OR_COMPANY` | Enrich Person or Company | Enrich person or company data using LinkedIn URLs, emails, domains, or names. Returns enriched data including: contact information, LinkedIn profiles, company details, work experience, education, phone numbers (with phoneFull), and company metrics. Input types: LinkedIn profile URL (~100% success), email (~60% success), name+company (~90% success), company domain/name, or SIREN number (French companies). |
| `DATAGMA_FIND_WORK_EMAIL` | Find Work Email | Find verified work email address for a person using their name and company. Returns a professionally verified email address with validation metadata including SMTP checks and MX records. Requires either fullName or firstName+lastName, plus company domain or LinkedIn company slug. |
| `DATAGMA_GET_CREDITS` | Get Credits | Get the current credit balance for the authenticated Datagma API account. Use this to check how many API credits remain before making enrichment calls. |
| `DATAGMA_GET_TWITTER_BY_EMAIL` | Get Twitter Profile By Email | Retrieve Twitter account information associated with an email address. This action looks up Twitter username and display name for a given email address using Datagma's enrichment database. Returns Twitter username, display name, and the queried email if a match is found, or status 'NOT_FOUND' if no Twitter account is associated with the email. Use this when you need to: - Find someone's Twitter handle from their email address - Verify if an email has an associated Twitter account - Enrich contact data with social media information |
| `DATAGMA_GET_TWITTER_BY_USERNAME` | Get Twitter Profile by Username | Enrich Twitter profile data using Datagma's database. Returns contact information (email), social media profiles (LinkedIn, Facebook, GitHub), and professional details (skills, interests, industry) associated with a Twitter username. Note: Not all usernames are in Datagma's database. A 'not found' response (code 5) indicates the username hasn't been indexed yet. |
| `DATAGMA_REVERSE_PHONE_LOOKUP` | Reverse Phone Lookup | Tool to reverse-lookup information associated with a phone number. Use when you have a phone number and need associated details (e.g., carrier, location). |
| `DATAGMA_SEARCH_PHONE_NUMBERS` | Search Phone Numbers | Find mobile phone numbers using email address and/or LinkedIn profile URL. Returns list of phone numbers with confidence scores and optional WhatsApp verification. Best results when both email and LinkedIn URL are provided. |

## Supported Triggers

None listed.

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

The Datagma MCP server is an implementation of the Model Context Protocol that connects your AI agent to Datagma. It provides structured and secure access so your agent can perform Datagma 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 Datagma 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 Datagma.
```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 datagma.
- The router checks the user's Datagma connection and prepares the MCP endpoint.
- The returned session.mcp.url is the MCP URL that your agent will use to access Datagma.
- This approach keeps things lightweight and lets the agent request Datagma tools only when needed during the conversation.
```python
# Create a Datagma Tool Router session
session = composio.create(
    user_id=user_id,
    toolkits=["datagma"]
)

mcp_url = session.mcp.url
```

```typescript
// Create Tool Router session for Datagma
const session = await composio.create(userId as string, {
  toolkits: ['datagma'],
});
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 Datagma. "
        "Help users perform Datagma 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 Datagma. Help users perform Datagma 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=["datagma"]
)
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 Datagma. "
        "Help users perform Datagma 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: ['datagma'],
  });
  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 Datagma. Help users perform Datagma 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 Datagma MCP with OpenAI Agents SDK to build a functional AI agent that can interact with Datagma.
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 Datagma MCP Agent with another framework

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

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- [Coinmarketcal](https://composio.dev/toolkits/coinmarketcal) - CoinMarketCal is a community-powered crypto calendar for upcoming events, announcements, and releases. It helps traders track market-moving developments and stay ahead in the crypto space.
- [Control d](https://composio.dev/toolkits/control_d) - Control d is a customizable DNS filtering and traffic redirection platform. It helps you manage internet access, enforce policies, and monitor usage across devices and networks.
- [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights.
- [Databricks](https://composio.dev/toolkits/databricks) - Databricks is a unified analytics platform for big data and AI on the lakehouse architecture. It empowers data teams to collaborate, analyze, and build scalable solutions efficiently.
- [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.
- [Dovetail](https://composio.dev/toolkits/dovetail) - Dovetail is a research analysis platform for transcript review and insight generation. It helps teams code interviews, analyze feedback, and create actionable research summaries.
- [Dub](https://composio.dev/toolkits/dub) - Dub is a short link management platform with analytics and API access. Use it to easily create, manage, and track branded short links for your business.
- [Elasticsearch](https://composio.dev/toolkits/elasticsearch) - Elasticsearch is a distributed, RESTful search and analytics engine for all types of data. It delivers fast, scalable search and powerful analytics across massive datasets.
- [Fireflies](https://composio.dev/toolkits/fireflies) - Fireflies.ai is an AI-powered meeting assistant that records, transcribes, and analyzes voice conversations. It helps teams capture call notes automatically and search or summarize meetings effortlessly.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Datagma MCP?

With a standalone Datagma MCP server, the agents and LLMs can only access a fixed set of Datagma tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Datagma 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 Datagma tools.

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

Yes, absolutely. You can configure which Datagma 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 Datagma data and credentials are handled as safely as possible.

---
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