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Documentation Index

Fetch the complete documentation index at: https://docs.fastapps.org/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

FastApps allows you to integrate external MCP servers as APIs in your widgets using Metorial. Metorial is an open-source integration platform that provides access to 600+ MCP servers with enterprise-grade observability and scaling. With FastApps + Metorial integration, you can:
  • Access hundreds of verified MCP servers (Slack, Gmail, Google Calendar, etc.)
  • Use external MCP servers directly as APIs in your tools
  • Get instant deployment with built-in scaling
  • Monitor and debug with detailed logging

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Quick Setup

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1. Generate Integration File

Run the FastApps CLI command to generate the Metorial integration: 
fastapps use metorial
This creates a metorial_mcp.py file under the /api folder in your project.

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2. Configure Environment Variables

Add your Metorial API key, OpenAI API key, and deployment ID to your environment: 
# .env
METORIAL_API_KEY=your_metorial_api_key
OPENAI_API_KEY=your_openai_api_key
METORIAL_DEPLOYMENT_ID=your_deployment_id
Get your Metorial API key and deployment ID at https://metorial.com/

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3. Use in Your Widgets

Import and use the Metorial integration in your FastApps tools: 
from server.api.metorial_mcp import call_metorial

# Use in your widget
result = await call_metorial("Search Hackernews for latest AI discussions")

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Generated File Structure

When you run fastapps use metorial, a metorial_mcp.py file is created in the /server/api folder: /server/api/metorial_mcp.py 
import os
import asyncio
from metorial import Metorial
from openai import AsyncOpenAI


async def call_metorial(
    message: str,
    deployment_id: str = None,
    model: str = "gpt-4o",
    max_steps: int = 25
):
    # Get credentials from environment
    metorial_api_key = os.getenv('METORIAL_API_KEY')
    openai_api_key = os.getenv('OPENAI_API_KEY')
    deployment_id = deployment_id or os.getenv('METORIAL_DEPLOYMENT_ID')
    
    if not all([metorial_api_key, openai_api_key, deployment_id]):
        raise ValueError("Missing environment variables: METORIAL_API_KEY, OPENAI_API_KEY, METORIAL_DEPLOYMENT_ID")
    
    # Initialize clients
    metorial = Metorial(api_key=metorial_api_key)
    openai = AsyncOpenAI(api_key=openai_api_key)
    
    # Run query
    response = await metorial.run(
        message=message,
        server_deployments=[deployment_id],
        client=openai,
        model=model,
        max_steps=max_steps
    )
    
    return response.text

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What This File Does

The metorial_mcp.py file provides a simple API wrapper to:
  1. Load Credentials - Automatically loads API keys and deployment ID from environment variables
  2. Simple Interface - Provides a clean call_metorial() function with minimal parameters
  3. Handle Authentication - Validates required environment variables
  4. Return Results - Processes and returns text responses from MCP servers

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Using MCP Servers in Widgets

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Basic Example

Here’s how to integrate external MCP servers in your FastApps widget: 
from fastapps import BaseWidget
from pydantic import BaseModel, Field
from server.api.metorial_mcp import call_metorial

class NewsSearchInput(BaseModel):
    query: str = Field(..., description="Search query for news")

class NewsSearchWidget(BaseWidget):
    identifier = "news-search"
    title = "Search News"
    input_schema = NewsSearchInput
    invoking = "Searching..."
    invoked = "Search complete!"
    
    async def execute(self, input_data: NewsSearchInput, ctx):
        # Simple usage - uses default deployment ID from environment
        result = await call_metorial(f"Search for: {input_data.query}")
        
        return {
            "query": input_data.query,
            "results": result
        }

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Custom Deployment

Use a specific deployment ID or customize the model: 
from fastapps import BaseWidget
from pydantic import BaseModel, Field
from server.api.metorial_mcp import call_metorial

class CustomSearchInput(BaseModel):
    query: str = Field(..., description="Search query")
    use_mini: bool = Field(default=False, description="Use mini model")

class CustomSearchWidget(BaseWidget):
    identifier = "custom-search"
    title = "Custom Search"
    input_schema = CustomSearchInput
    
    async def execute(self, input_data: CustomSearchInput, ctx):
        # Customize deployment and model
        result = await call_metorial(
            message=f"Find latest: {input_data.query}",
            deployment_id="custom_deployment_id",  # Optional: override default
            model="gpt-4o-mini" if input_data.use_mini else "gpt-4o",
            max_steps=10
        )
        
        return {
            "query": input_data.query,
            "results": result
        }

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Learn More

For complete documentation and advanced features, visit: Metorial Documentation: https://metorial.com/

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Next Steps

Tool Basics

Back to Tool Basics

API Integration

External API Integration

Advanced Patterns

Advanced Tool Patterns