field_benchmark

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

With no annotations provided, the description must convey behavioral traits such as side effects, return values, or error conditions. It only discloses that probes need explicit opt-in and that results are aggregated by cohort. It does not describe what the tool returns, whether it is read-only, or any side effects. The sparse disclosure leaves the agent uncertain about the tool's full behavior.

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 two short sentences with no redundant wording. The primary action is stated first, followed by a specific constraint. Every word adds some value, and the structure is front-loaded with the main purpose.

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 three parameters, no output schema, and no annotations, the description is insufficient. It does not explain what manifest_path should reference, what run_probes does beyond the opt-in hint, or the role of review_path. An agent would need external knowledge to invoke this correctly. The description covers only a fraction of the necessary context for a tool of this complexity.

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 input schema has 0% description coverage, so the description must explain the parameters. It does not define manifest_path, run_probes, or review_path beyond their names. The mention of cohort and probe opt-in hints at some parameter usage, but not enough for an agent to correctly fill in all parameters without further knowledge. The description compensates poorly for the missing schema documentation.

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 states a specific verb ('Aggregate') and resource ('field benchmark reports') with a clear qualifier ('by cohort'). It distinguishes the tool from siblings like create_benchmark_template or field_campaign_status, which deal with creation or status rather than aggregation. The probe opt-in clause adds a specific characteristic, but the description does not explicitly name any sibling for comparison.

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 no guidance on when to use this tool over alternatives. It states a requirement about probes ('probes require explicit MCP probe opt-in') but does not explain when aggregation is appropriate or when to choose another tool. There is no mention of context, prerequisites, or exclusions.

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