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

Significantly exceeds the annotations. It discloses the data-retention model (submissions stored as a pseudonymised cross-platform record), what is deliberately not stored (no IP address), the consent model for the handle and self-declared software label (opt-in only, grades never published without opt-in), and the grading mechanics the eval band drives. These are exactly the side effects an agent should know before writing to a persistent record.

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 outcome is front-loaded — grade and returned fields come first, mechanics and privacy after — and every sentence carries real information. It is dense and the final privacy sentence is a long em-dash run-on, so it is efficient but not effortlessly readable.

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?

With 12 required parameters and no output schema, the description correctly spends space enumerating the return surface (indicative grade, expected-loss band, rated grade, reasons, remediation steps) and the rules that bind the grade. It omits any pointer to sibling tools for interpreting results, which keeps it from fully covering the workflow.

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 100% with 15 enums, so the schema already carries the parameter definitions in full. The description adds only a few points the schema also states (eval bands, publication consent, stored labels), so it neither compensates for a gap nor adds much beyond the structured fields. Baseline 3 applies.

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 verb and resource — scoring your own agent deployment from structured answers — and enumerates exactly what comes back (indicative grade, expected-loss band, rated grade, top 3 reasons, steps to raise the grade). It is clear on its own, but it never names or contrasts with the siblings explain_grade and get_scale, leaving the agent to infer the boundary.

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?

Usage is implied rather than stated: the agent is told this is a self-scoring submission, but there is no explicit 'use this when' guidance, no prerequisites (e.g. run get_scale first to understand the grades), and no exclusion pointing to explain_grade for interpreting a result. Adequate but leaves routing to inference.

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