argot.get_fit_status

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds that the tool only diagnoses and never fits or writes, which is consistent. It also mentions 'structured reasons and next_action' in output, but beyond that, no new behavioral traits are disclosed.

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 sentences, front-loaded with key purpose, no fluff. Efficiently covers tool scope, output, and boundaries.

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 0 parameters, comprehensive annotations, and no output schema, the description fully explains what the tool does and its diagnostic role. It mentions output format and exclusion of mutations, which is sufficient for this 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?

The tool has 0 parameters with 100% schema coverage (empty). The description adds value by describing what the tool returns (structured reasons, next_action), which is unnecessary but helpful. Baseline for 0 params is 4.

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 gets 'repository readiness' and lists specific aspects (fit suitability, completeness, configuration compatibility, adaptive refresh recommendation). It also explicitly says what it does not do ('diagnoses setup and maintenance only; it never fits or writes'), distinguishing it from sibling tools like check_changeset or check_hunk.

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 says to use 'before using learned tools,' which provides clear context. It also states it 'diagnoses setup and maintenance only,' implying when not to use for fitting/writing. However, no explicit alternatives are mentioned.

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