Weather Data
Enables asynchronous, fault-tolerant tool calls for historic weather data gathering within a Claude Desktop environment.
About
This application addresses the fundamental limitations of synchronous LLM tool calling, particularly within MCP Servers, by leveraging Resonate's durable promises. It transforms blocking tool calls into resilient, asynchronous background jobs, ensuring robustness against common distributed system failures like timeouts, application errors, and process crashes. By demonstrating a three-tool pattern (`start_gathering`, `probe_status`, `await_result`) for a long-running task like historic weather data collection, it showcases how to build durable, distributed systems that integrate seamlessly with Claude Desktop's tool calling convention.
Key Features
- Converts synchronous LLM tool calls into durable asynchronous operations.
- 0 GitHub stars
- Seamlessly integrates with Claude Desktop's MCP Server environment.
- Provides built-in mechanisms for fault tolerance, including supervision, retries, and idempotency.
- Supports long-running background jobs for AI agents without blocking.
- Implements a promise-based pattern (start, probe, await) for managing job lifecycle.
Use Cases
- Implementing reliable, non-blocking tool calls for AI agents.
- Gathering historic weather data or other long-running data collection tasks.
- Building distributed systems that require robust LLM tool integration.
