College Scorecard

get_field_of_study

Get earnings and debt data by field of study from the College Scorecard.

Returns median earnings and median debt for graduates of specific academic
programs. Can be filtered by school, by field name, or both.

Args:
    school_id: College Scorecard school ID to see field-level data for one school.
    field: Field of study name or keyword to search across schools
           (e.g. 'Computer Science', 'Nursing', 'Business').

Input Schema

TableJSON Schema

Name	Required	Description	Default
`field`	No
`school_id`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4/5.0

Behavior3/5

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

With no annotations, the description carries the burden. It discloses return values (median earnings and debt) and filtering capability. However, it does not mention behavior with no filters, data limits, or error conditions, leaving some ambiguity.

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

Conciseness4/5

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

The description is well-structured: a clear purpose statement, a brief explanation of return data, and a compact Args list. Every sentence adds value, and it is front-loaded with the most important information.

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

Completeness3/5

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

The description covers the core functionality and parameters. Since an output schema exists, return details aren't needed. However, it doesn't state default behavior with no filters or edge cases, making it slightly incomplete for a fully self-contained description.

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

Parameters4/5

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

The schema description coverage is 0%, and the description compensates by providing meaningful parameter explanations with examples, such as 'field: Field of study name or keyword to search across schools (e.g. 'Computer Science'...'. It clarifies the purpose of each parameter and their optional combination, though it doesn't fully specify the interaction logic.

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 ('Get') and resource ('earnings and debt data by field of study'). It distinguishes from sibling tools by focusing on field-level data rather than school comparisons or details.

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 explains that it can be filtered by school, field, or both, giving clear usage context. However, it does not explicitly mention alternative tools or when not to use it, so it scores slightly lower than a perfect 5.

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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