faf_score

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuinely non-structured behavioral facts: the operation is deterministic (same .faf always scores the same) and it returns score, tier, and per-slot breakdown. It does not mention auth/permission needs or rate limits, but the determinism disclosure is valuable added context.

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?

Three tight sentences, front-loaded with the verb and scoring basis. The 'how much project DNA an agent has before it has to guess' metaphor follows the already-explicit '33-slot context model' and is mildly redundant, but every other clause carries real information.

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 no output schema, the description correctly compensates by naming the return values (score, tier, per-slot breakdown), and the single optional param is fully schema-documented. The main gap is that it never states which project or .faf artifact is scored, which is non-obvious for a zero-required-param tool.

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?

Only one optional boolean parameter and schema description coverage is 100%, so the schema already documents 'details' fully; the baseline is 3. The description references a 'per-slot breakdown' return, which loosely maps to details=true, but adds no explicit semantics for the parameter itself.

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?

States a specific verb (score) and resource (project AI-readiness) with a named, concrete scoring basis: the fixed 33-slot context model and a 0–100% range. This is clearly distinguishable from siblings like faf_trust and refresh_faf, which imply different operations.

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 only implied: an agent can infer it is for assessing readiness, but there is no explicit when-to-use, when-not-to-use, or routing to alternatives such as faf_trust (which presumably evaluates a related quality dimension). No prerequisites or calling context are given.

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