cocomo_validate

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds context by detailing what the tool reports (MAPE, bias, per-type accuracy, coefficient adjustments) and the datasets it runs against (NASA93, COCOMO81, Albrecht, Kemerer). This provides useful behavioral information beyond annotations, though it does not describe potential output structure or edge cases.

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 extremely concise, consisting of three sentences with no redundant information. It is front-loaded with the core purpose in the first sentence, and each subsequent sentence adds specific value (datasets, reported outputs).

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?

For a tool with one optional parameter and no output schema, the description covers the essential aspects: what it validates, against which datasets, and what it reports. It mentions coefficient adjustments, implying actionable output. However, it does not clarify how the COCOMO model is specified (e.g., from a prior estimate) or explain the output structure in detail, leaving minor gaps.

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 100%, so the baseline is 3. The description mentions the datasets in the tool's body but does not add meaning to the 'dataset_filter' parameter beyond what the schema already provides ('Optional filter to validate against specific datasets only'). No extra semantics are given for the parameter's usage or format.

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 specifies the tool's purpose: validating the COCOMO estimation model against a set of 195 historical projects. It lists the datasets used and the metrics reported (MAPE, bias, per-type accuracy, coefficient adjustments). This differentiates it from siblings like 'cocomo_estimate' (which would produce estimates) and 'cocomo_ground_truth' (which might provide ground truth), though explicit contrast is absent.

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 the tool is for validation, but it does not explicitly state when to use it versus alternatives (e.g., 'Use this to assess model accuracy; for new estimates, use cocomo_estimate'). It lacks when-not-to-use guidance and does not mention any prerequisites or exclusions.

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