recomendar_tools

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

There are no annotations, so the description carries the full burden. It discloses that the tool uses AI to understand intent and suggests the most adequate tools from mcp-brasil, which implies a non-destructive read-only operation. However, it does not describe the output structure (though an output schema exists) or any limitations of the AI-based recommendation process.

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 well-structured. It front-loads the core purpose in the first sentence, adds a brief clarifying sentence about the AI mechanism, and then presents the argument documentation in a clean, scannable format. No word is wasted.

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?

For a simple one-parameter recommender, the description adequately covers the main functionality, the input format, and the source of recommendations (mcp-brasil). The presence of an output schema reduces the need to document return values. It could explicitly differentiate from 'search_tools', but overall it is sufficiently complete for an agent to select and invoke it.

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 input schema provides zero description for the single 'query' parameter, but the description compensates with an 'Args' section that explains the parameter meaning and gives a concrete example: 'quero dados sobre gastos do governo federal'. This fully covers the parameter's semantics.

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: 'Recomenda tools relevantes a partir de uma pergunta em linguagem natural' (Recommends relevant tools from a natural language question). It uses a specific verb ('recomenda') and resource ('tools'), and distinguishes itself from siblings like 'search_tools' by emphasizing AI-based intent comprehension and providing explanations for each recommendation.

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 does not provide any guidance on when to use this tool versus alternatives such as 'search_tools' or 'listar_features'. It mentions that the tool will explain 'quando e como usar cada uma' (when and how to use each recommended tool), but this refers to the output content, not to the circumstances under which an agent should invoke this tool itself.

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