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Glama

rebanho

Livestock herds and animal products by state (IBGE PPM).

Instructions

Livestock herds and animal products by state (IBGE PPM).

    Products: bovino, bubalino, equino, suino_total, suino_matrizes, caprino, ovino, galinaceos_total, galinhas, galinhas_poedeiras, codornas, leite, ovos_galinha, ovos_codorna, mel, casulos, la.

    Args:
        produto: Herd or product (e.g. "bovino", "leite")
        ano: Reference year (default: latest published)
    

Input Schema

NameRequiredDescriptionDefault
anoNo
produtoYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.2.0

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden, and it does disclose the data source (IBGE PPM), the geographic granularity (by state), and that 'ano' defaults to the latest published year. It does not describe validation of 'produto', error behavior, or pagination, and the stated default ('latest published') conflicts with the schema default of 0, which is a mild ambiguity an agent must resolve.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose statement is front-loaded in the first sentence, and the long product enumeration earns its space by substituting for a missing enum. The 'Args:' block is slightly verbose but well structured and contains no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return-value documentation is not required, and for a read-only data-retrieval tool the source, granularity, valid inputs, and default are the essentials. The only residual gap is the unexplained mismatch between the documented default and the schema's numeric default of 0.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it largely does: it enumerates all 17 valid values for 'produto' (acting as an inline enum) and gives examples plus the default semantics for 'ano'. It adds meaning well beyond the bare schema, though it does not explain how the sentinel value 0 for 'ano' maps to 'latest published'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a concrete resource (livestock herds and animal products) and its granularity (by state) and source (IBGE PPM), so an agent knows exactly what dataset is returned. It does not explicitly differentiate itself from siblings like producao_anual or exportacao, but those cover clearly different datasets, so confusion is unlikely.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance, no mention of prerequisites, and no pointer to alternative tools such as producao_anual or listar_produtos. Usage is only implied by the data domain, leaving the agent to infer the right context.

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