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

With no annotations provided, the description fully carries the burden of behavioral disclosure. It reveals key behaviors: the default epoch is the final one, the naming convention for constraint security and predictive performance metrics, and crucially that missing metrics are returned as null instead of the -1 sentinel used by the training script. This eliminates ambiguity and prevents misinterpretation of sentinel values.

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 composed of three sentences that are concise and front-loaded with the primary purpose. It wastes no words, though it could be slightly more structured by separating the parameter default from the metric naming conventions. Overall, it is efficient and well-organized.

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 2 parameters, no output schema, and no annotations, the description covers the default behavior, metric naming, and null handling. However, it fails to describe the return format (e.g., dictionary or list), possible error conditions (e.g., invalid experiment_id), or the full set of metrics. It is adequate but leaves gaps for an agent to infer.

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 0%, so the description must compensate. It does so by explaining the default behavior of the epoch parameter (defaulting to final epoch) and implicitly linking experiment_id to the run. However, it does not explicitly describe the parameters' types, constraints, or the experiment_id parameter's purpose beyond the tool context. The added value is moderate but not fully compensatory.

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 retrieves metrics for a specific epoch of a run, defaulting to the final epoch. This is a specific verb-resource combination that distinguishes it from siblings like list_experiments (listing all experiments) or get_experiment_config (getting configuration). The mention of defaulting to the final epoch adds precision.

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 no explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it, nor does it reference sibling tools like compare_runs or search_logic_definitions. The usage context is only implied by the tool's purpose (getting metrics), but no exclusions or prerequisites are stated.

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