faf_score

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

With no annotations, the description carries the full burden. It explicitly states determinism ('the same .faf always scores the same') and that it returns score, tier, and per-slot breakdown. It does not mention side effects, auth needs, or rate limits, but none are expected.

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?

Two concise sentences: first defines purpose and scale, second adds determinism and return info. No fluff, front-loaded with key 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?

Given no output schema, the description adequately explains return values (score, tier, per-slot breakdown). It covers the single parameter's effect implicitly. Could mention tier categories but sufficient for a simple 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?

Schema coverage is 100% for one optional parameter. The description aligns with the schema ('Returns the score, tier, and per-slot breakdown') but adds no additional meaning beyond what the parameter description already provides.

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 uses a specific verb 'Score' and resource 'project's AI-readiness', clearly defines the 0–100% scale and the 33-slot context model. It distinguishes this tool from siblings by its unique assessment focus.

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 implies usage for scoring AI-readiness but provides no explicit guidance on when to use versus alternatives like faf_trust or faf_init. No exclusions or context are given.

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