get_data_coverageDescribes actual publishable sheep, goat, and cattle market, species, date coverage, and freshness. Configured sources are not counted as covered until eligible observations exist.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| meta | Yes | ||
| citation | Yes |
Changes observed during successful MCP inspections.
Output schema / properties / citation / properties / attribution_text / maxLengthPrevious value: -1000New value: +20000Output schema / properties / meta / properties / applied_filters / additionalProperties / anyOfPrevious value: -[
- {
- "maxLength": 256,
- "type": "string"
- },
- {
- "type": "number"
- }
-]New value: +[
+ {
+ "maxLength": 1024,
+ "type": "string"
+ },
+ {
+ "type": "number"
+ }
+]Output schema / properties / meta / properties / next_cursor / anyOfPrevious value: -[
- {
- "maxLength": 256,
- "type": "string"
- },
- {
- "type": "null"
- }
-]New value: +[
+ {
+ "maxLength": 1024,
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, closed-world, non-destructive behavior, so safety is covered. The description adds genuinely useful semantics beyond that: configured sources are not counted as covered until eligible observations exist, clarifying that 'coverage' means publishable data, not mere configuration.
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?
Two tight sentences with no waste, and the core scope is front-loaded ahead of the qualifying nuance. Every sentence earns its place.
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?
An output schema exists, so return values need not be explained, and a zero-parameter read tool has little else to disclose. The one gap is the absence of routing guidance relative to the sibling tools.
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 tool takes zero parameters, so the baseline is 4 and there is nothing for the description to disambiguate. No parameter information is needed or missing.
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?
States a specific verb (describes) and resource (publishable sheep, goat, and cattle market/species/date coverage and freshness). It is distinguishable from siblings like get_latest_prices and get_market_report, which return values rather than coverage, though it does not name a sibling explicitly.
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?
Usage is implied: an agent would call this to learn what data is actually available before querying prices or reports. However, the description never states when to use it versus the alternatives, nor any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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