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

With no annotations, the description carries full burden. It states the tool 'analyzes' samples, clearly implying a non-mutating read operation. It does not explicitly say 'read-only' or describe side effects, but the analysis-oriented language conveys behavior adequately for this type of tool.

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 efficient and well-structured: the first sentence states the core purpose, the second adds detail, and the third provides a useful contrast. Every sentence adds value with no redundancy.

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 simple with two parameters and an output schema. The description covers the core purpose, input requirements, and what it infers. It does not mention edge cases or prerequisites, but the schema handles parameter constraints, making this sufficient for an agent 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 input schema already provides descriptions for both parameters (samples and agent_name), achieving 100% coverage. The description repeats the 3-5 sample recommendation and adds context about the inference purpose, but adds little beyond the schema's existing detail.

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 the tool profiles an agent's personality from writing samples, listing specific outputs (Big Five traits, communication style, strengths, blind spots, persuasion profile). It explicitly contrasts with content analysis, making it distinct from siblings like analyze_content and analyze_persuasion.

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 frames the tool as the inverse of content analysis, implying when to use it (to understand the writer) vs. when not (to evaluate content effectiveness). It also provides a recommended sample count (3-5), but does not explicitly name alternative tools or list exclusions.

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