Python Tools

Python Tools allow you to create custom MCP tools by writing Python functions. With AI-assisted code generation, you can describe what you want in natural language and have Claude generate the implementation for you.

AI-Powered Tool Creation

Describe your tool in plain English and let AI generate the Python code

What Can Python Tools Do?

Python Tools are incredibly versatile. You can create tools that:

Data Processing

Parse and transform data formats
Calculate statistics and metrics
Filter and aggregate datasets

Text Operations

Extract information from text
Format and template strings
Validate input patterns

Calculations

Mathematical computations
Financial calculations
Unit conversions

Business Logic

Pricing calculations
Eligibility checks
Workflow decisions

Date & Time

Date calculations
Timezone conversions
Schedule generation

Custom Integrations

API wrapper functions
Data validation logic
Complex transformations

Step-by-Step Guide

Follow these steps to create a Python function tool:

Step 1: Select Python Function

Choose Python Function Tool Type

Open an existing MCP server (or create one using MCP Composer). Click Create New Tool and select "Python Function" to open the AI-assisted Python editor.

Step 2: Use AI to Generate Your Tool

Describe Your Tool to the AI Agent

The Python editor includes an embedded AI agent. Describe what you want in natural language, and the AI will generate:

Complete Python function implementation with type hints
Auto-generated input and output schemas
Input validation and error handling
Docstrings and inline documentation

Python tool creation with AI agent interface

Example prompt:"Create a function that calculates compound interest. It should take principal amount, annual interest rate, number of years, and compounding frequency as inputs."

You can iterate with the AI agent to refine the code, add features, or fix issues. The agent understands context and can make targeted improvements to your function.

Step 3: Configure Environment Variables (Optional)

Add Environment Variables

If your Python function needs API keys, tokens, or configuration values, add environment variables that your code can access at runtime.

Environment variables configuration dialog

Security: Environment variables are stored securely and injected at runtime. Use them for sensitive data like API keys instead of hardcoding in your Python function.

Step 4: Test and Publish

Test and Make Available

Before publishing, test your function thoroughly:

Use the built-in test interface with sample inputs
Verify the output matches expectations
Test edge cases (empty values, null, large numbers, etc.)
Click Save when satisfied with the results
Set status to Published to make available to agents

AI-Powered Features

Natural Language Input

Describe your tool in plain English—no Python expertise required to get started.

Conversational Refinement

Iterate with the AI to add features, fix bugs, or optimize your code.

Automatic Schema Generation

Input/output schemas are generated from your function signature—fully editable.

Context-Aware Assistance

The AI understands your server context and can create tools that integrate with your API endpoints.

Example AI Prompts

Effective Prompts for the AI Agent

Data Validation

"Validate email addresses and return validity status, domain, and username."

Date Calculation

"Calculate business days between two dates, excluding weekends and holidays."

Text Processing

"Extract and standardize phone numbers from text strings."

Financial Logic

"Calculate monthly loan payments from principal, interest rate, and term."

Pro Tip: Be specific about inputs, outputs, and edge cases. The more detail you provide, the better the generated code will be.

Best Practices

Building Effective Python Tools

Working with the AI Agent

Be specific and detailed in your descriptions
Explicitly request validation and error handling
Ask for edge case handling upfront
Iterate conversationally to refine the code

Tool Design

Keep each function focused on one task
Use descriptive names for tools and parameters
Write clear descriptions for agent understanding
Document return values and their meanings

Testing: Always test with edge cases—empty values, null inputs, extreme numbers, and invalid data types before publishing.