openrouter-function-calling skill

Overview

This skill shows how to implement function and tool calling with OpenRouter-supported models to build agents and structured outputs. It provides practical patterns for wiring model-invoked functions, validating inputs/outputs, and integrating with existing Python workflows. Use it to enable model-driven tool execution and predictable structured responses.

How this skill works

The implementation registers callable tools or functions and exposes schemas so OpenRouter models can select and invoke them when appropriate. The model returns structured function calls that the agent code parses, executes, and then supplies the result back to the model for follow-up. Error cases and validation steps ensure safe, auditable execution and clear fallback behavior.

When to use it

• Building conversational agents that must perform actions or fetch live data
• Producing structured outputs (JSON) from model responses for downstream processing
• Integrating model-driven automation into CI/CD, devops, or data pipelines
• Connecting language models to external tools, APIs, or system commands
• Prototyping multi-step workflows where the model orchestrates tool calls

Best practices

• Register a small, well-documented set of tools with explicit input/output schemas
• Validate and sanitize all model-provided arguments before executing any tool
• Log each function call, input, and output for observability and debugging
• Implement timeouts, rate limits, and permission checks for risky actions
• Test with unit and integration tests that simulate both normal and erroneous model calls

Example use cases

• Agent that queries a ticketing system to open/update incidents via function calls
• Automation that converts natural language tasks into structured API calls for deployments
• Data enrichment pipeline where the model calls a lookup tool and returns normalized JSON
• Monitoring agent that calls diagnostic tools and summarizes results in structured reports

FAQ