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Documentation Index

Fetch the complete documentation index at: https://docs.logic.inc/llms.txt

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

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Overview

Logic MCP servers support asynchronous task execution, allowing AI platforms to start long-running tasks without blocking while they complete. Instead of waiting for completion, the AI platform receives an execution ID and can check back later for results.

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How It Works

When an AI platform executes a Logic agent through MCP:
  1. Task starts immediately - Logic begins processing
  2. Execution ID returned - AI platform receives a unique identifier
  3. Status checking - Use check_execution_status tool with the execution ID
  4. Results retrieved - When complete, the tool returns the output

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Example Flow

AI Platform → Start Logic agent execution
Logic MCP → Returns execution_id: "exec_abc123"
AI Platform → check_execution_status("exec_abc123")
Logic MCP → Status: "running"
[wait a bit]
AI Platform → check_execution_status("exec_abc123")
Logic MCP → Status: "completed" + results

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Running Multiple Tasks

The async model enables parallel execution. AI platforms can:
  • Start multiple Logic agents simultaneously
  • Poll each execution ID separately
  • Retrieve results as each task completes
Example: Process 10 images in parallel instead of sequentially
  • Without async: 10 × 30 seconds = 5 minutes total
  • With async: ~30 seconds total (parallelized)

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Platform Support

Different AI platforms handle async tasks automatically:
  • Claude platforms: Automatically poll and manage multiple executions
  • ChatGPT: Check platform documentation for async behavior
  • Custom clients: Implement check_execution_status tool and polling logic

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Next Steps