autotune_gauge

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

No annotations provided, so description carries full burden. It clearly states what it sets (min/max, color zones) and the heuristic for orientation (higher-is-better vs worse) based on field name. Could mention potential fallback behavior when field_name doesn't match.

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?

Very concise: one sentence for purpose and a bullet-like list for parameters. Front-loaded with key action. Every word adds value, no redundancy.

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 complexity (3 params, no annotations, output schema exists), description covers the core functionality well. Minor gap: does not explicitly state that it modifies the buffer in place, but it's implied. Output schema likely handles return values.

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 0%, but description provides meaningful explanations for all three parameters: control_id (id of gauge), samples_json (JSON array with example), field_name (unit/direction hint, defaults to id). This fully compensates for missing schema descriptions.

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?

Verb 'tune' with specific resource 'buffer gauge' and clear actions: sets min/max and color zones based on percentiles. Distinguishes from siblings like autowire_buffer by focusing on gauge calibration from samples.

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?

Implies usage when you have observed samples and want to auto-configure a gauge, but no explicit guidance on when not to use it or alternatives among many sibling tools.

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