generate_icp

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

Annotations already mark readOnlyHint=false and destructiveHint=true, lowering the burden on the description. The description adds valuable behavioral detail beyond annotations: it explains that results are saved, can be reused via create_campaign(icp_id=...), and that decision_makers_only changes persona seniority levels. It does not clarify what destructive effect may occur, but it does not contradict the annotations either.

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 well structured, leading with the core purpose, then output characteristics, then reusable result, then a compact Args section. The only slight redundancy is the sentence listing overlapping capabilities such as 'buyer persona creation, ideal customer profiling, and B2B market research,' which adds retrieval keywords but little semantic value.

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?

For a tool with one required parameter, an output schema present, and moderate complexity, the description is effectively complete. It tells the agent what to pass, what each optional input does, what output to expect, and what happens to the result. The only minor omission is the unexpanded destructiveHint, but the annotations already flag that behavior.

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 carries the full burden, and it delivers. Every parameter gets a meaningful explanation with examples: target_description shows concrete phrasing, company_context explains its precision benefit, focus_query gives sample focus areas, and decision_makers_only spells out seniority levels plus when False is appropriate. This far exceeds what the bare schema provides.

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 uses a specific verb and resource: 'Generate a rich Ideal Customer Profile with buyer personas,' and goes further by specifying the shape of the output (2-4 personas with pain points, fears, barriers, LinkedIn search parameters, and confidence scores). It also distinguishes itself from siblings by noting the result is saved and reusable with create_campaign, so an agent can tell it apart from related tools like icp or campaign without inspecting schemas.

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 clearly communicates the broad applicability: buyers, candidates, research participants, hiring managers, investors, or partners. It also lists supported use cases like target audience analysis and customer segmentation. However, it does not explicitly state when not to use this tool or name alternative tools to prefer in specific situations.

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