Multi-year trend for one school sourced from the Urban Institute Education Data Portal (IPEDS). Choose a metric (enrollment, graduation_rate, retention, cost) and a year range.
| Name | Required | Description | Default |
|---|---|---|---|
| metric | Yes | Trend metric. | |
| unit_id | Yes | IPEDS UNITID. | |
| end_year | Yes | Last academic year, e.g. 2022. | |
| start_year | Yes | First academic year, e.g. 2010. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds beyond the annotations by specifying the data source (Urban Institute Education Data Portal/IPEDS) and the single-school scope, which is useful behavioral context.
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 with no wasted words. It front-loads the core purpose and source, then lists the parameter choices and range selection, making it easy for an agent to parse quickly.
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 annotations, complete parameter schema, and simple read-only nature of the tool, the description is largely sufficient for selection and invocation. However, there is no output schema and the description does not describe the response format or behavior for unavailable data, which is a minor gap.
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% with each parameter already described, including the metric enum and year examples. The description simply restates the metric names and 'year range' without adding deeper semantic meaning beyond the schema, so baseline 3 is appropriate.
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 returns a multi-year trend for one school and identifies the data source and available metrics. It is specific enough to distinguish from college_search and college_compare, though it does not explicitly name those siblings.
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 by saying 'for one school' and instructing the user to choose a metric and year range. It does not, however, explicitly state when not to use it or point to alternatives such as college_compare for comparisons or college_metrics for other metric views.
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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