zyta_minerva_consultar

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

With no annotations, the description carries the full burden. It discloses that the tool uses RAG, adds curated regulatory framework, generates AI response, and logs to user history. However, it does not mention side effects (e.g., non-idempotent POST), authentication requirements, or rate limits. The behavioral traits are partially covered but not exhaustively.

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 extremely concise: two sentences. The first sentence delivers the core purpose with action verbs, and the second adds critical context about history logging and the HTTP method. No redundant or irrelevant 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 no output schema, the description lacks details on the response format or error handling. It explains the general process (RAG, regulatory framework, AI generation) but does not specify what the user receives. The logging behavior is good, but for a tool that returns content, the output structure is undefined. This leaves an information gap for the agent.

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 coverage is 100% with clear descriptions for each parameter. The tool description adds no additional meaning beyond the schema; it only mentions the query implicitly. Baseline of 3 is appropriate as the schema already documents parameters well, and the description does not enhance understanding of parameter usage.

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: a complete legal consultation that searches rulings (RAG), adds curated regulatory framework, and generates an AI response. It explicitly mentions the POST endpoint and logging to user history. This differentiates it from sibling tools like zyta_minerva_buscar_fallos, which likely only searches rulings without the full consultative response.

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 indicates this tool is for a comprehensive legal query that produces an AI-generated response and logs history. It implicitly distinguishes from zyta_minerva_buscar_fallos but does not explicitly state when to use each. The context of 'completa' and the mention of generating a response provide clear usage cues, though explicit exclusions are missing.

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