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

It discloses that the tool automatically detects language/framework, extracts key info, and ranks results by votes and relevance. With no annotations provided, these details are valuable, though it doesn't clarify interaction with optional language/framework parameters or rate limits.

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

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

The description is moderately concise with bullet points and examples, but could be slightly more streamlined. It front-loads the key action and uses examples effectively.

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

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

Given the complexity (5 params, 2 required, output schema), the description covers the main purpose and behavior but lacks explanation for the reasoning parameter and how optional parameters override auto-detection. Output schema existence reduces need for return value details, but there are still gaps.

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

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

Examples illustrate use of language, framework, and reasoning, but the description does not explicitly define the purpose of the reasoning parameter, and language/framework are mentioned as auto-detected without clarifying that they are optional overrides. Schema coverage is 0%, so more explicit parameter descriptions would help.

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

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

The description clearly states it finds solutions for error messages and stack traces from Stack Overflow and GitHub, with automatic detection and ranking. This distinguishes it from siblings like web_search or package_search which are more general.

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

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

The 'Perfect for' section lists specific use cases (debugging production errors, understanding cryptic messages, etc.), implicitly guiding when to use. However, it does not explicitly contrast with sibling tools or state when not to use it.

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