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MCI Python Adapter - Quickstart Guide

Welcome to the MCI Python Adapter! This guide will help you get started quickly with installing, configuring, and using the MCI (Model Context Interface) adapter to define and execute tools in your Python applications.

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Installation

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Prerequisites

  • Python 3.11 or higher
  • uv package manager (recommended) or pip
First, install uv if you haven’t already: 
# macOS or Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Or using Homebrew on macOS
brew install uv
Then install the MCI Python adapter: 
# Install from PyPI
uv pip install mci-py

# Or install with uv add (if using uv project)
uv add mci-py

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Option 2: Using pip

pip install mci-py

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Verify Installation

import mcipy
print("MCI Python Adapter installed successfully!")

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Quick Example

Here’s a complete example to get you started in under 5 minutes:

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1. Create a Tool Schema File

Create a file named my-tools.mci.json: 
{
  "schemaVersion": "1.0",
  "metadata": {
    "name": "My First Tools",
    "description": "A simple collection of tools"
  },
  "tools": [
    {
      "name": "greet_user",
      "description": "Generate a personalized greeting",
      "inputSchema": {
        "type": "object",
        "properties": {
          "username": {
            "type": "string",
            "description": "The user's name"
          }
        },
        "required": ["username"]
      },
      "execution": {
        "type": "text",
        "text": "Hello, {{props.username}}! Welcome to MCI."
      }
    },
    {
      "name": "get_weather",
      "description": "Fetch weather information",
      "inputSchema": {
        "type": "object",
        "properties": {
          "location": {
            "type": "string",
            "description": "City name"
          }
        },
        "required": ["location"]
      },
      "execution": {
        "type": "http",
        "method": "GET",
        "url": "https://api.example.com/weather",
        "params": {
          "location": "{{props.location}}"
        }
      }
    }
  ]
}

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2. Write Python Code

Create a file named example.py: 
from mcipy import MCIClient

# Initialize the client
client = MCIClient(
    schema_file_path="my-tools.mci.json",
    env_vars={
        "API_KEY": "your-secret-key"
    }
)

# List available tools
print("Available tools:")
for tool_name in client.list_tools():
    print(f"  - {tool_name}")

# Execute a tool
result = client.execute(
    tool_name="greet_user",
    properties={"username": "Alice"}
)

# Check the result
if result.result.isError:
    print(f"Error: {result.result.content[0].text}")
else:
    print(f"Success: {result.result.content[0].text}")

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3. Run Your Code

python example.py
Output: 
Available tools:
  - greet_user
  - get_weather
Success: Hello, Alice! Welcome to MCI.

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What’s Next?

Now that you’ve seen the basics, explore these resources to learn more:

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Key Concepts at a Glance

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Execution Types

MCI supports four execution types:
  • Text: Return templated text directly
  • File: Read file contents with template substitution
  • CLI: Execute command-line tools
  • HTTP: Make HTTP API requests

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Template Placeholders

Use placeholders in your configurations:
  • {{props.fieldName}} - Access input properties
  • {{env.VARIABLE_NAME}} - Access environment variables

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Tool Organization

  • Tools: Individual actions defined in your schema
  • Toolsets: Reusable collections of tools in separate files
  • MCP Servers: Integration with external MCP servers

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Common Use Cases

  • API Integration: Use HTTP execution to integrate REST APIs
  • DevOps Automation: Use CLI execution for system tasks
  • Configuration Management: Use File execution for config templates
  • Reporting: Use Text execution for formatted reports
  • Data Processing: Combine multiple execution types

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Getting Help

If you encounter issues or have questions: Happy building with MCI! 🚀