agrobr-mcp

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 mentions the data source (CEPEA/ESALQ) and default/max values for 'dias', but doesn't cover important behavioral aspects like rate limits, authentication requirements, error conditions, response format, or whether this is a read-only operation. For a tool with no annotation coverage, this represents significant gaps in transparency.

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 well-structured with clear sections (purpose, products list, args explanation) and uses bullet-like formatting for the product list. It's appropriately sized with no wasted sentences, though the product list is quite long (15 items) which slightly affects conciseness while being necessary for completeness.

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 moderate complexity (2 parameters, no annotations, but has output schema), the description provides good coverage of what the tool does and its parameters. The presence of an output schema means the description doesn't need to explain return values. However, it could better address behavioral aspects like data freshness, source reliability, or usage constraints to be fully complete.

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?

With 0% schema description coverage, the description fully compensates by providing comprehensive parameter semantics. It clearly explains 'produto' as 'Commodity name' with a complete list of 15 valid values, and 'dias' as 'Number of recent days to return' with specific default (5) and max (60) values. This adds substantial meaning beyond what the bare schema provides.

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 with specific verbs ('Daily spot prices') and resources ('Brazilian agricultural commodities'), and distinguishes it from siblings by specifying the data source (CEPEA/ESALQ). It provides a comprehensive list of 15 specific products, making the scope explicit and differentiated from other tools like 'futuros_b3' or 'producao_anual'.

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 usage context by specifying it's for 'daily spot prices' and listing products, suggesting when to use it for recent commodity pricing. However, it doesn't explicitly state when NOT to use it or mention alternatives like 'futuros_b3' for futures data or 'producao_anual' for annual production data, leaving some guidance gaps.

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