generate_persona

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

No annotations are provided, so the description must fully disclose behavioral aspects. It mentions that missing information will be explicitly marked as 'to be verified', which is a useful behavioral trait. However, it does not disclose other critical aspects such as data sources, whether it performs web searches, or any dependencies on existing data. For a generation tool, the lack of clarity on data sourcing is a notable gap.

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 concise, consisting of two sentences. It effectively front-loads the core purpose and includes a key behavioral note about marking missing info. The language is clear and to the point, with no unnecessary fluff. It could be slightly more structured but is appropriately brief.

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?

There is no output schema, so the description must fully explain what the tool returns. It describes the content of the persona page (decision maker, pain points, etc.), which is helpful, but it lacks details on formatting, length, or how the output is delivered (e.g., as a text block). With 6 parameters and no annotations, the description leaves gaps that an agent might need to call it correctly.

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 has 0% description coverage, meaning the description must add meaning to the parameters. The description clarifies that the output is a 'one-page persona' and specifies the content areas (decision maker, pain points, etc.), which gives context to what parameters like known_facts and decision_maker_role contribute. However, it does not detail each parameter's specific usage, but the overall purpose helps infer their roles.

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 explicitly states the tool's purpose: to generate a one-page client/account persona including decision maker, pain points, entry points, and objections. It clearly identifies the resource (a persona document) and the action (generate). While it doesn't explicitly distinguish from siblings, the specific output format and content make it unique enough among the listed tools.

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 provides no guidance on when to use this tool versus alternatives like research_company, web_search, or draft_outreach. It does not state prerequisites (e.g., need initial research) or indicate that this is for client-facing documents. Users are left to infer that it might be used after research, but no explicit direction is given.

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