Every loaded year of the provincial minimum wage (UMP) for one province, oldest first, each row with its source (governor decree or BPS table) and URL. 1997 to 2026. Without province_code: the same rows for all provinces in one response, ordered by province then year (for charts and bulk analysis).
| Name | Required | Description | Default |
|---|---|---|---|
| province_code | No | Kemendagri province code; omit for all provinces |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well: it discloses the year range (1997–2026), row contents (source type and URL), ordering (oldest first; by province then year when all provinces), and fallback behavior when province_code is omitted. It omits details on data completeness, error handling, or freshness, keeping it from a perfect score.
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 sentences, front-loaded with the core purpose and then the parameter-omission behavior. Every clause earns its place by conveying scope, ordering, row contents, date range, and fallback usage.
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 one optional parameter, no annotations, and no output schema, the description is largely complete: it explains what the tool returns, how rows are ordered, and what happens without a province_code. It could say more about data freshness, missing years, or error behavior, but it is sufficient for an agent to invoke it correctly.
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?
Schema coverage is 100%, so the schema already defines province_code as a Kemendagri code and says to omit for all provinces. The description adds meaning beyond the schema by specifying the ordering and use case (charts and bulk analysis) when the parameter is omitted, and implying single-province filtering when it is provided.
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 states a specific resource and scope: every loaded year of provincial minimum wage (UMP) for one province, oldest first. It distinguishes this historical series from siblings like get_ump (current) and get_umk_history (city-level UMK) through its explicit focus on UMP and years.
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?
It clearly states the two use cases: with province_code for a single province, and without province_code for all provinces ordered by province then year, explicitly noting the latter is for charts and bulk analysis. It does not name alternative tools or exclusions, but provides clear context for when to use each parameter state.
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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