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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context by stating that the tool requires 'Zero additional computation required' and 'Does not consume a query allocation', which are important cost-related side effects. The phrase 'so future predictions improve' also indicates a learning/feedback effect, further increasing 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 exceptionally concise: three sentences with no redundant words. It front-loads the primary purpose ('Complete the prediction loop — report inference diagnostics'), then provides supporting details efficiently. Every sentence adds value, and there is no repetition of schema information.

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 the tool has 8 parameters, no output schema, and no annotations, the description should provide more guidance to ensure successful invocation. It explains the main diagnostic metrics but omits crucial context for the required prediction_hash field and other optional fields. The phrase 'Complete the prediction loop' is somewhat abstract and does not specify the required identifier or the expected response, leaving an agent under-equipped for accurate use.

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 schema has 0% coverage in the description, but the description explicitly names three key parameters: ESS ratio, PSIS-khat, and log-likelihood gap, which map directly to the schema fields ess_ratio, psis_khat, and log_lik_gap. It also implies approach_taken via 'the approach FactorGuide recommended'. However, other parameters like prediction_hash, n_replications, runtime_seconds, and actual_mse_ratio are not explained, leaving gaps for an agent trying to fill them correctly.

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 states the tool's function: reporting inference diagnostics after running FactorGuide's recommended approach. It specifies the resource ('inference diagnostics'), the action ('report'), and the outcome ('future predictions improve'). This distinguishes it from sibling tools like factorguide_diagnose or factorguide_explain, which are likely for other stages of the workflow.

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 provides a clear when-to-use condition: 'After running the approach FactorGuide recommended'. It also notes that the tool 'Does not consume a query allocation', implying it is low-cost. However, it does not explicitly state when not to use this tool or name alternatives, so it falls short of a perfect 5.

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