aiworkhub_source_graph_retrieval_eval

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

The annotations already declare readOnlyHint=true, and the description repeats READ-ONLY rather than adding to it. It does add some behavioral context by explaining that each declared case attempt is bound to a canonical acceptance authority and that accepted_outcome_coverage is measured from canonical task evidence, but it omits failure modes, permissions, and where registered cases come from.

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 short and front-loads the operation in the first sentence. The second sentence is dense with internal identifiers and backticked field names, which hurts readability, but it is not bloated and its content is relevant.

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?

An output schema exists, so return values do not need explanation, and zero parameters make invocation trivial. However, the description does not explain where registered cases come from, what the precision@k/MRR evaluation covers, or how this tool differs from the many sibling quality/eval tools, leaving material context 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?

The tool has zero parameters, and the schema has no properties to document. With no parameter semantics needed, the baseline score of 4 is appropriate; the description does not need to compensate for missing schema detail.

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 and resource: run registered precision@k/MRR cases through the manager MCP wrapper. However, it never names or distinguishes the many sibling evaluation/quality tools, such as aiworkhub_risk_mode_precision_bench or aiworkhub_quality_run_checks, so an agent must infer when this evaluation is the right choice.

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?

There is no explicit when-to-use, when-not-to-use, prerequisite, or alternative-tool guidance. The mention of registered cases and measuring accepted_outcome_coverage implies a purpose but does not give an agent a selection rule.

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