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

Beyond the annotations (which only indicate false readOnly/destructive flags), the description discloses that it performs live searches (an external effect), consumes no credits but has daily/monthly caps, and conditionally omits opportunity score and cluster placement when no research run exists. This is crucial behavioral context that annotations alone cannot convey.

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 dense but efficiently organized: core purpose first, then usage guidance, then prerequisites, then exclusions. Every sentence adds essential information without redundancy. No fluff or filler.

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 description covers the tool's purpose, usage context, prerequisites, exclusions, output behavior, cost implications, and conditional results. With no output schema, it adequately describes the return values (volume bands, ranking pages, and conditional opportunity score). There is nothing an agent needs to know that is missing.

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?

The schema only defines two UUID parameters with no descriptions (0% coverage). While the description doesn't explicitly map parameter names, it clearly implies that suggestionId refers to the article idea under evaluation and projectId to the containing project. Given the low param count and obvious names, this implicit context is adequate, though explicit param explanations would earn a 5.

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 states a specific action (attach real search data to an existing article idea), the mechanism (one live Google search per target keyword), and the outputs (volume bands and ranking pages). It clearly distinguishes itself from siblings like start_research_run and create_article_suggestion_with_input, making its role unambiguous.

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?

It explicitly states when to use the tool ('after create_article_suggestion_with_input, or on any existing suggestion...'), when not to use it ('Do not use to find new ideas or map a niche — that is start_research_run'), and the prerequisite for full output (a completed research run). This leaves no ambiguity for the agent.

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