codif_get_training_data

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, indicating a safe, read-only operation with open-world data. The description adds context by specifying it downloads a 'muestra' (sample), suggesting limited data retrieval rather than full datasets, which is useful beyond annotations. However, it doesn't detail aspects like rate limits, authentication needs, or exact output format.

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 a single, efficient sentence in Spanish that directly states the tool's function without unnecessary details. It's front-loaded with the core action ('Descarga una muestra') and specifies the resource and classifiers concisely, making it easy to understand at a glance.

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's complexity (2 parameters, no output schema) and rich annotations (read-only, non-destructive, open-world), the description is adequate but has gaps. It explains what the tool does but lacks details on output format, sample size, or error handling. With annotations covering safety, it's minimally viable but could benefit from more context for full 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?

Schema description coverage is 100%, with clear descriptions for both parameters: 'classification' (enum with 'caenes' or 'ciuo') and 'modelVersion' (optional integer for version). The description adds minimal value beyond the schema by mentioning the classifiers, but doesn't explain parameter interactions or provide additional semantics like what 'latest' means for modelVersion. Baseline 3 is appropriate given high schema coverage.

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 purpose: 'Descarga una muestra de los sets train/test usados por el clasificador CAENES o CIUO' (Downloads a sample of the train/test sets used by the CAENES or CIUO classifier). It specifies the verb (download), resource (train/test sets), and target classifiers, but doesn't explicitly differentiate from sibling tools like 'codif_classify_activity' or 'datos_get_dataset' which might serve different purposes.

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 guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or contexts where this tool is preferred, such as for obtaining training data versus performing classifications with 'codif_classify_activity' or querying datasets with 'datos_query_resource'. Usage is implied but not explicitly stated.

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