# How to integrate Wolfram alpha api MCP with OpenAI Agents SDK

```json
{
  "title": "How to integrate Wolfram alpha api MCP with OpenAI Agents SDK",
  "toolkit": "Wolfram alpha api",
  "toolkit_slug": "wolfram_alpha_api",
  "framework": "OpenAI Agents SDK",
  "framework_slug": "open-ai-agents-sdk",
  "url": "https://composio.dev/toolkits/wolfram_alpha_api/framework/open-ai-agents-sdk",
  "markdown_url": "https://composio.dev/toolkits/wolfram_alpha_api/framework/open-ai-agents-sdk.md",
  "updated_at": "2026-05-12T10:30:27.354Z"
}
```

## Introduction

This guide walks you through connecting Wolfram alpha api to the OpenAI Agents SDK using the Composio tool router. By the end, you'll have a working Wolfram alpha api agent that can solve a complex calculus equation, get current weather in paris, convert 100 usd to euros today through natural language commands.
This guide will help you understand how to give your OpenAI Agents SDK agent real control over a Wolfram alpha api account through Composio's Wolfram alpha api MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Wolfram alpha api with

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

The Wolfram alpha api MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Wolfram|Alpha account. It provides structured and secure access to computational knowledge, so your agent can perform actions like running complex calculations, generating data visualizations, retrieving factual information, converting units, and solving equations on your behalf.
- Instant factual queries and lookups: Let your agent fetch reliable answers to questions about science, math, history, geography, and more using Wolfram|Alpha’s expert knowledge base.
- Complex mathematical computations: Have your agent solve equations, compute derivatives or integrals, and process advanced mathematical queries with step-by-step solutions.
- Data analysis and visualization: Request charts, graphs, or plots generated from live data or mathematical functions, all directly through your agent.
- Unit conversions and calculations: Ask your agent to instantly convert units, currencies, or perform engineering calculations for seamless workflow integration.
- Scientific and statistical analysis: Empower your agent to perform statistical tests, analyze datasets, or provide scientific constants and reference data without manual lookup.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `WOLFRAM_ALPHA_API_ASYNC_POD_FETCH` | Async Pod Fetch | Fetch a single asynchronous pod from Wolfram\|Alpha Full Results API. Use this tool to retrieve individual pod computations that were marked as async in a Full Results query. When you query the Full Results API with async=true, pods that take longer to compute return async URLs containing 'id' and 's' tokens. Use those tokens with this tool to fetch the computed pod content. Typical workflow: (1) Query Full Results API with async=true parameter, (2) Parse response for async pod URLs, (3) Extract id and s tokens from URLs like 'asyncPod.jsp?id=MSPa...&s=13', (4) Use this tool with those tokens. |
| `WOLFRAM_ALPHA_API_ESTABLISH_CONNECTION` | Establish Wolfram\|Alpha Connection | Tool to store Wolfram\|Alpha AppID into the connection credential store. Use when establishing or updating your AppID so that subsequent actions automatically include this credential. |
| `WOLFRAM_ALPHA_API_EXTRACT_RECALC_URL_TOKENS` | Extract Recalculate URL & Tokens | Tool to extract the recalculate URL and id/s tokens from full Wolfram\|Alpha results. Use when you need to follow up with recalc.jsp or relatedQueries.jsp calls. |
| `WOLFRAM_ALPHA_API_FULL_RESULTS_RECALCULATE` | Full Results Recalculate | Recalculate a prior WolframAlpha Full Results query to retrieve additional computational results (pods). Use this action when: - A previous Full Results API query timed out or returned incomplete results - You have a recalculate URL or ID token from a previous query - You need to fetch more computational pods from the same query The action requires the 'id' token (obtained from EXTRACT_RECALC_URL_TOKENS or from a Full Results response). The 's' parameter is optional and often not needed. Returns raw XML containing the recalculated query results, which may include additional pods or error information. |
| `WOLFRAM_ALPHA_API_FULL_RESULTS_RELATED_QUERIES` | Full Results Related Queries | Tool to fetch related query suggestions for a previous Full Results computation. Use after obtaining `id` and `s` from a Full Results API response. |
| `WOLFRAM_ALPHA_API_GET_APP_ID` | Get Wolfram\|Alpha AppID | Tool to fetch the Wolfram\|Alpha AppID from credentials. Use when you need to verify the current AppID before making API calls. |
| `WOLFRAM_ALPHA_API_QUERY_LLM_API` | Query LLM API | Tool to query Wolfram\|Alpha LLM API for computed knowledge optimized for large language model consumption. Returns plain text results with query interpretations, computed data, and image URLs. Use when you need comprehensive computational knowledge formatted for AI agent processing. |
| `WOLFRAM_ALPHA_API_QUERY_SUMMARY_BOX` | Query Summary Box | Tool to query the Summary Boxes API for pre-generated XHTML boxes summarizing Wolfram\|Alpha knowledge. Use when you need formatted summary information for subjects like countries, chemicals, dates, or people. Requires a valid summary box path from Query Recognizer API. |
| `WOLFRAM_ALPHA_API_SHORT_ANSWERS_RESULT` | Short Answers Result | Tool to fetch a concise textual answer from Wolfram\|Alpha. Use when you need a short, direct response. |
| `WOLFRAM_ALPHA_API_SPOKEN_RESULTS_RESULT` | Get Spoken Result | Tool to retrieve a spoken-style single-sentence result from Wolfram\|Alpha. Use when you need a concise, conversational answer to a natural-language query. |
| `WOLFRAM_ALPHA_API_VALIDATE_QUERY` | Validate Query | Tool to validate a Wolfram\|Alpha query, returning parsing assumptions and warnings. Use when you need to check query parse before a full computation. |

## Supported Triggers

None listed.

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

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

mcp_url = session.mcp.url
```

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

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

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- [Builtwith](https://composio.dev/toolkits/builtwith) - BuiltWith is a web technology profiler that uncovers the technologies powering any website. Gain actionable insights into analytics, hosting, and content management stacks for smarter research and lead generation.
- [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation.
- [Cabinpanda](https://composio.dev/toolkits/cabinpanda) - Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Wolfram alpha api MCP?

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

### Can I manage the permissions and scopes for Wolfram alpha api while using Tool Router?

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

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