mureo_outcome_evaluate

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

With no annotations provided, the description fully carries the behavioral burden. It discloses deterministic behavior, the built-in direction semantics (lower-is-better vs higher-is-better), volume-only metrics being 'reported, never scored,' and the noise_pct threshold with zero/absent baseline resulting in 'inconclusive' to avoid fabricating swings. This is exemplary transparency.

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 description is dense but well-structured: a lead purpose statement, usage instruction, platform neutrality, behavioral rules, and edge cases. Each clause earns its place without filler, and the information is front-loaded with the most critical details.

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?

Despite lacking an output schema, the description explains the output classification (improved/regressed/inconclusive), the default noise threshold, platform flexibility, and edge cases like zero/absent baseline. This is complete for an evaluation tool with object inputs.

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% with detailed property descriptions (e.g., before contains examples and typical source). The description adds semantic value by clarifying the direction mapping for metric names, noise_pct default, and that before is usually metrics_at_action. This goes beyond the schema but relies on the schema's existing clarity, hence a 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 opens with a specific verb and resource: 'Deterministically evaluate whether a logged action's outcome improved, regressed, or is inconclusive.' It clearly distinguishes this tool from siblings by positioning it as 'the reproducible verdict the observation-window review (daily-check) and /learn rely on,' setting it apart from raw reporting or analytics tools.

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 gives clear context for when to use: pass before/after metrics to get a reproducible verdict for daily-check and /learn, and notes it works for any platform. It even contrasts with 'eyeballing the numbers.' However, it does not explicitly name alternative tools or state when not to use it, so it falls just short of a 5.

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