# How to integrate Convex MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Convex MCP with OpenAI Agents SDK",
  "toolkit": "Convex",
  "toolkit_slug": "convex",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/convex/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/convex/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-06-18T09:21:44.444Z"
}
```

## Introduction

This guide walks you through connecting Convex to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Convex agent that can list records from convex tasks table, run convex query for active users, inspect convex deployment function logs through natural language commands.
This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Convex account through Composio's Convex MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Convex with

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

The Convex MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Convex account. It provides structured and secure access so your agent can perform Convex operations on your behalf.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `CONVEX_CREATE_DEPLOY_KEY` | Create deploy key | Tool to create a deploy key for use with the Convex CLI. Use when you need to generate credentials for CLI-based development or deployment workflows. The generated key provides administrative access to the specified deployment. |
| `CONVEX_CREATE_DEPLOYMENT` | Create Deployment | Tool to create a new deployment for a Convex project. Use when you need to create a development, production, or custom deployment. Specify the deployment type and optional configuration like class, reference, and region. |
| `CONVEX_CREATE_PROJECT` | Create Project | Tool to create a new project on a Convex team, optionally provisioning a dev or prod deployment. Use when you need to initialize a new Convex project in a team. |
| `CONVEX_DELETE_CUSTOM_DOMAIN` | Delete Custom Domain | Tool to remove a custom domain from a Convex deployment. Use when you need to delete a previously configured custom domain. |
| `CONVEX_DELETE_DEPLOYMENT` | Delete Deployment | Tool to delete a Convex deployment. Use when you need to permanently remove a deployment and all its data. WARNING: This action will delete all data and files in the deployment and cannot be undone. |
| `CONVEX_DELETE_PROJECT` | Delete project | Deletes a Convex project and all its deployments permanently. Use when you need to permanently remove a project and all associated data. This operation cannot be undone. |
| `CONVEX_EXECUTE_QUERY_BATCH` | Execute Query Batch | Tool to execute multiple Convex query functions in a single batch request. Use when you need to fetch data from multiple queries efficiently in one API call. |
| `CONVEX_GET_DEPLOYMENT` | Get Deployment Details | Tool to retrieve details about a Convex cloud deployment. Use when you need to get information about a specific deployment including its configuration, region, creation time, and status. |
| `CONVEX_GET_PROJECT_BY_ID` | Get Project by ID | Tool to retrieve detailed information about a specific Convex project by its ID. Use when you need to fetch project metadata including name, slug, team association, and creation time. |
| `CONVEX_GET_PROJECT_BY_SLUG` | Get Project by Slug | Tool to retrieve a Convex project by its slug within a team. Use when you need to fetch project details using human-readable identifiers instead of numeric IDs. |
| `CONVEX_GET_QUERY_TIMESTAMP` | Get Query Timestamp | Tool to get the latest timestamp for queries from Convex deployment. Use when you need to retrieve the current query timestamp from the Convex API. |
| `CONVEX_GET_TOKEN_DETAILS` | Get token details | Tool to retrieve token details for the authenticated token. Returns the team ID for team tokens or project ID for project tokens. Especially useful after receiving a token from an OAuth flow to identify which team or project it belongs to. |
| `CONVEX_LIST_DEPLOY_KEYS` | List Deploy Keys | Tool to list all deploy keys for a specified Convex deployment. Use when you need to view all authentication tokens that can be used to deploy to this deployment. |
| `CONVEX_LIST_DEPLOYMENT_CLASSES` | List deployment classes | Tool to list available deployment classes for a Convex team. Use when you need to check which deployment classes are available for a specific team. |
| `CONVEX_LIST_DEPLOYMENT_REGIONS` | List deployment regions | Tool to list available deployment regions for a Convex team. Use when you need to check which regions are available for deploying a team's backend. |
| `CONVEX_LIST_DEPLOYMENTS` | List Deployments | Tool to list all deployments for a Convex project. Use when you need to see all deployments (production, preview, or local) for a specific project. |
| `CONVEX_LIST_LOG_STREAMS` | List Log Streams | Tool to list all existing log stream configurations in a deployment. Use when you need to view configured log streaming destinations like Datadog, Webhook, Axiom, or Sentry. |
| `CONVEX_LIST_PROJECTS` | List Projects | Tool to list all projects for a specific Convex team. Use when you need to retrieve all projects associated with a team by team ID. |
| `CONVEX_UPDATE_DEPLOYMENT` | Update Deployment | Tool to update properties of an existing Convex deployment. Use when you need to modify deployment settings such as dashboard edit confirmation or deployment reference. Only the fields provided in the request are modified. |

## Supported Triggers

None listed.

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

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

mcp_url = session.mcp.url
```

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

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

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- [Appveyor](https://composio.dev/toolkits/appveyor) - AppVeyor is a cloud-based continuous integration service for building, testing, and deploying applications. It helps developers automate and streamline their software delivery pipelines.
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- [Bitbucket](https://composio.dev/toolkits/bitbucket) - Bitbucket is a Git-based code hosting and collaboration platform for teams. It enables secure repository management and streamlined code reviews.
- [Blazemeter](https://composio.dev/toolkits/blazemeter) - Blazemeter is a continuous testing platform for web and mobile app performance. It empowers teams to automate and analyze large-scale tests with ease.

## Frequently Asked Questions

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

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

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

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

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)