pbi_analyze_model_quality

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 lists what is checked but does not state whether the tool is read-only, what it returns (beyond detection), whether it modifies anything, or what the output format looks like. The output schema exists but is not visible to the agent from the description alone.

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 concise and front-loaded. The first sentence states the purpose, and the second lists concrete examples. Every sentence provides useful information without redundancy or unnecessary detail.

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 covers the core purpose and the types of checks performed, which is adequate for a simple analysis tool. However, missing parameter semantics and lack of behavioral details (e.g., read-only, return behavior) leave gaps in selecting and invoking the tool correctly. The output schema likely covers return values, but this is not referenced in the description.

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 one parameter, 'source', with a default of 'live' and 0% description coverage. The description does not mention this parameter at all, leaving its meaning, possible values, and impact undocumented. Since schema coverage is low, the description fails to compensate for this gap.

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: 'Detecta problemas tipicos del modelo (calidad)' and enumerates specific checks (measures without folders, long DAX, bidirectional/inactive relationships, calculated columns, visible IDs, missing calendar). This specific verb+resource combination distinguishes it from sibling tools like pbi_audit_model or pbi_health_check by focusing on model quality patterns.

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 a use case ('revisa...') but does not explicitly state when to use this tool over alternatives, nor does it mention prerequisites or exclusions. It is clear from context that it is for analyzing model quality, but no when-not-to-use guidance is provided.

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