cv_screener

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds some context: the payment requirement hints at transactional behavior and potential costs. However, it doesn't describe key traits like how the scoring works (e.g., algorithm, scale), what the output includes (e.g., scores, feedback), error handling, or rate limits. For a tool with no annotations and complex functionality, this is insufficient.

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 appropriately sized and front-loaded: the first sentence states the core purpose, and the second adds a critical constraint (payment). There's no wasted text, and both sentences earn their place by providing essential information. However, it could be slightly more structured (e.g., separating purpose from requirements), but it's efficient overall.

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?

Given the complexity (CV analysis and scoring), no annotations, no output schema, and 4 parameters, the description is incomplete. It lacks details on behavioral aspects (e.g., scoring methodology, output format), parameter usage, and how it fits among siblings. The payment note is helpful, but for a tool with significant functionality gaps, more context is needed to make it fully actionable.

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 75% (3 out of 4 parameters have descriptions), so the baseline is 3. The description doesn't add meaning beyond the schema: it mentions CV and job description but doesn't explain parameter interactions (e.g., how 'cvBase64' and 'mimeType' relate, or what 'signature' is for beyond the schema's note). It compensates slightly by implying the tool uses these inputs, but not enough to exceed the baseline.

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's purpose: 'Analyzes and scores a CV against a job description.' It specifies the verb ('analyzes and scores'), resource ('a CV'), and target ('against a job description'), making the function unambiguous. However, it doesn't differentiate from sibling tools like 'lead_scorer' or 'interview_questions', which might involve similar scoring or analysis tasks, so it doesn't reach the highest score.

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 minimal usage guidance: it mentions a payment requirement ('Requires payment (0.001 SOL)'), which is a prerequisite but not a contextual guide. It doesn't specify when to use this tool versus alternatives (e.g., 'lead_scorer' for leads, 'interview_questions' for generating questions), nor does it outline exclusions or ideal scenarios for CV screening. This lack of comparative context limits its helpfulness.

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