validate_output

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

With no annotations provided, the description carries the burden of behavioral disclosure. It transparently states that the tool evaluates output quality and lists the dimensions it checks, giving the agent a clear sense of its non-mutating evaluation behavior. However, it does not describe the return value format, whether any side effects exist, or how scores are computed, leaving significant behavioral details undisclosed.

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 sentences with no filler. The first sentence front-loads the primary purpose, and the second efficiently enumerates the specific checks. Every phrase adds useful information, and it is easy to scan quickly.

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?

The description gives a solid overview of what the tool evaluates, but the absence of an output schema and annotations means the agent is left without key context such as the format or meaning of the returned score, thresholds, or how to interpret results. Given the nested criteria object and no output schema, more detail on the output would improve completeness.

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 already provides 100% coverage with descriptions for all parameters, so the baseline is 3. The description adds value by summarizing how the parameters map to high-level checks (e.g., 'claim density' corresponds to factual_claims_count and 'task relevance' relates to task_description), making the semantics more accessible. This goes beyond simply restating the schema.

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 uses a specific verb ('Score') and clearly identifies the resource ('agent output quality') and the configurable criteria. It lists concrete checks (length, keywords, forbidden patterns, claim density, task relevance), which makes the purpose clear. However, it does not explicitly distinguish this tool from sibling tools like check_scope_compliance or check_hallucination_risk, so it falls short of full differentiation.

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 when to use the tool by enumerating the checks it performs, so an agent could infer it is appropriate for general output validation against criteria. However, it provides no explicit guidance on when not to use it or what alternatives exist (e.g., check_hallucination_risk for specific hallucination checks). The context is implied rather than clearly stated.

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