Retrieve livestock inventory, slaughter, and production data for cattle, hogs, chickens, milk, and eggs. Filter by year, state, and category.
Get livestock data — inventory, slaughter, production. Commodities: CATTLE, HOGS, CHICKENS, MILK, EGGS
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | Year | |
| state | No | State code. Omit for national | |
| category | No | INVENTORY, PRODUCTION, SALES | |
| commodity | Yes | CATTLE, HOGS, CHICKENS, MILK, EGGS |
Changes observed during successful MCP inspections.
v2026.4.11Input schema / additionalPropertiesRemoved value: -falsev2026.3.9Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish that the operation is read-only, idempotent, and non-destructive, so no safety disclosure is needed. The description adds little behavioral context beyond what the schema lists, and it introduces a misleading 'slaughter' category that conflicts with the schema's valid categories; it also says nothing about output shape, defaults, or state/national behavior.
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 two compact sentences that front-load the core action and then give the exact commodity scope. There is no filler or repetition of the schema.
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?
With a fully described schema and strong read-only annotations, an agent can assemble a valid request (commodity is required; year, state, and category are optional). It is still only minimally complete because it does not describe what the returned records look like, does not distinguish itself from usda_prices/usda_ag_query, and contains the slaughter/SALES discrepancy.
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?
Since schema description coverage is 100%, the schema already documents all four parameters and the description does not add semantic value. Worse, the description's category list ('slaughter') does not match the schema's category description (SALES), which could lead an agent to supply an invalid category value.
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 uses a specific verb and resource ('Get livestock data') and enumerates data categories and commodities, making it clear this is the USDA livestock-oriented tool rather than crop or price tools. It does not explicitly compare itself with siblings like usda_prices or usda_ag_query, and the 'slaughter' category clashes with the schema's SALES value, so it falls short of a 5.
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 when to use the tool—when livestock inventory, slaughter, or production figures are requested—and it scopes commodities to cattle, hogs, chickens, milk, and eggs. It never states exclusions or points to alternatives such as usda_prices or usda_ag_query, so routing among the many USDA siblings is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.