prelaunch_check

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

With no annotations, the description carries the full behavioral burden. It discloses key execution behavior: LLM-powered intent parsing depends on API keys, GitHub token raises rate limits, Google search falls back to DuckDuckGo, and depth modes change sources and analysis. This is strong transparency, though it stops short of covering failure modes, rate-limit consequences, or what happens to output quality when optional keys are absent.

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 front-loaded with a one-sentence summary, then organized into Uses, environment variables, Args, and Returns sections. Every section serves a purpose for correct invocation. Nothing is wasted, and the format makes it easy for an agent to scan.

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?

The tool is moderately complex due to environment dependencies, depth modes, and multi-source scanning. The description covers prerequisites, optional configuration, parameter behavior, and the return report's contents. Given the rich output description, the agent has enough context to invoke it correctly and interpret results, even without a shown output schema.

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?

Schema description coverage is 0%, so the description must fully compensate. It does: idea_text is defined as a natural-language description, and depth gets a complete enum explanation with behavior for quick, standard, and deep. This is more meaningful than the bare schema alone and leaves no parameter ambiguity.

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 opens with a specific verb and resource: 'Check if a product idea already exists before building it.' It clearly defines the tool's job and reinforces it with concrete use cases (discuss ideas, check competition, validate demand, find prior art), so an agent can distinguish its purpose from any generic search or analysis tool.

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 description lists explicit use cases and gives depth-level guidance, even recommending 'standard' as the preferred mode. There are no sibling tools to differentiate against, so explicit exclusions are not expected. What is missing is guidance about when this tool would not be appropriate, but the provided usage context is clear and actionable.

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