College Scorecard

compare_schools

Compare multiple colleges or universities side by side.

Returns key metrics for each school including tuition, admission rate,
median earnings, debt, and completion rate for easy comparison.

Args:
    school_ids: Comma-separated list of College Scorecard school IDs
                (e.g. '166027,166683,243780'). Use search_schools to find IDs.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`school_ids`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently explains the return data (key metrics like tuition and admission rate) and the input format, giving the agent a good understanding of expected behavior. It doesn't discuss error handling or side effects, but for a read-only comparison tool, the disclosed behavior is sufficient.

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

Conciseness5/5

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

The description is concise and front-loaded with a clear purpose, followed by return-value details and parameter guidance. Every sentence adds value, and the structure leads with the primary verb, making it easy to scan and understand.

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

Completeness5/5

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

Given the tool has a single parameter and an output schema (making detailed return listing unnecessary), the description is complete. It explains the purpose, the input source, and the type of output, enabling an agent to select and invoke the tool correctly without extra external knowledge.

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

Parameters5/5

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

Although the schema description coverage is 0%, the description fully compensates by clearly explaining the parameter `school_ids` with its format (comma-separated list), a concrete example, and a pointer to `search_schools` for finding valid IDs. This is excellent parameter documentation.

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

Purpose5/5

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

The description clearly states the tool's function with a specific verb ('Compare') and a well-defined resource ('multiple colleges or universities side by side'). It also lists the exact metrics returned, making the purpose unambiguous and distinguishing it from sibling tools like search_schools and get_school_detail.

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

Usage Guidelines4/5

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

The description gives clear context for when to use this tool (to compare multiple schools) and provides a prerequisite workflow by instructing the user to 'Use search_schools to find IDs.' It does not explicitly mention alternative tools for single-school comparisons, but the workflow guidance earns it a strong score.

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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TDQS

A4.4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: searching for schools, retrieving comprehensive details for one school, comparing multiple schools, and accessing field-of-study data. Even though get_school_detail and compare_schools both return earnings metrics, the descriptions and use cases (single vs. multiple schools) make them unambiguous.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case: search_schools, get_school_detail, compare_schools, get_field_of_study. There are no mixed conventions or vague names.

Tool Count5/5

With 4 tools, the server is well-scoped for its purpose. Each tool covers a distinct aspect of school data exploration (search, detail, comparison, field-level data), and the number falls comfortably within the ideal 3-15 range.

Completeness4/5

The tool set covers the core workflows: finding schools, retrieving detailed profiles, comparing schools, and examining field-of-study outcomes. Minor gaps exist, such as no support for historical trends or advanced filtering (e.g., by tuition range or size), but the surface is adequate for typical college research tasks.

Resources

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