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by aibtcdev

openrouter_integration_guide

Retrieve OpenRouter integration examples and code patterns to add AI features to Node.js, Cloudflare Workers, or browser projects, including API usage, model recommendations, and error handling.

Instructions

Get OpenRouter integration examples and code patterns for implementing AI features.

Use this tool when you need to add AI capabilities to any project. Returns:

  • Code examples for different environments (Node.js, Cloudflare Workers, browser)

  • API patterns and best practices

  • Model recommendations

  • Error handling patterns

This is a reference tool - use the returned code as a template for implementation.

Tip: call earning_opportunities to see how to put your assets to work.

Input Schema

NameRequiredDescriptionDefault
featureNoSpecific AI feature to implementall
environmentNoTarget environment for the integrationall

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.77.0

TDQS

A3.9/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the burden and does well by explicitly saying this is a reference tool and that returned code should be used as a template. It lists what the response includes, though it does not explicitly state that the tool is read-only or has no side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded and appropriately sized, with a clear purpose followed by a useful return-value list. The final tip about earning_opportunities is tangential and somewhat weakens focus, but the overall structure remains efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is complete enough for a reference/guide tool: it explains the purpose, usage context, and the kinds of content returned, compensating for the absence of an output schema. It still leaves minor gaps around sibling-tool choice and whether the guide has any execution or side-effect behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage and documents both optional enum parameters, so the schema already explains feature and environment. The description only indirectly references environments and AI features, which meets the baseline for high schema coverage without adding substantial parameter guidance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: getting OpenRouter integration examples and code patterns for implementing AI features. It clearly frames the tool as a reference resource, though it does not explicitly distinguish itself from the sibling openrouter_models tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a clear usage context: use this when you need to add AI capabilities to any project. It also labels the tool as a reference tool for templates, but it does not state when not to use it or name alternatives such as openrouter_models or scaffold_x402_ai_endpoint.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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