College Scorecard

search_schools

Search colleges and universities in the College Scorecard database.

Returns matching schools with basic info: name, location, size, tuition,
admission rate, median earnings, and completion rate.

Args:
    name: Full or partial school name to search (e.g. 'Harvard', 'community college').
    state: Two-letter US state abbreviation (e.g. 'CA', 'NY') to filter by state.
    zip_code: Five-digit ZIP code for location-based search. Use with distance parameter.
    distance: Search radius in miles from zip_code. Only used when zip_code is provided.
    degree_type: Filter by predominant degree type: '1' (certificate), '2' (associate),
                 '3' (bachelor), '4' (graduate). Omit for all types.
    limit: Maximum number of schools to return (default 25, max 100).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	No
`limit`	No
`state`	No
`distance`	No
`zip_code`	No
`degree_type`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses important behaviors: 'distance' is only used with 'zip_code', 'limit' has a default and maximum, and 'degree_type' filtering semantics. It also states what data is returned. However, it does not mention potential edge cases like no results or fuzzy matching, so it falls short of a 5.

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 well-structured with a clear purpose statement followed by an 'Args' list. Every sentence adds value, explaining parameters and returns without unnecessary fluff. The front-loaded main purpose ensures quick understanding.

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's complexity (6 optional parameters) and lack of annotations, the description is remarkably complete. It covers all parameters, explains return fields, and clarifies conditional logic (distance/zip_code). Even though an output schema exists but is not shown, the description's own output explanation suffices for the agent to call and interpret results.

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?

The schema provides zero description coverage, so the description fully compensates. Each parameter is explained with examples, conditional behavior, and allowed values: 'state' format, 'zip_code' and 'distance' relationship, 'degree_type' mapping, and 'limit' constraints. This adds significant meaning beyond the raw schema.

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 a specific verb ('Search') and a specific resource ('colleges and universities in the College Scorecard database'). It also lists the types of information returned, which distinguishes it from siblings like compare_schools or get_school_detail that serve different functions.

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

Usage Guidelines3/5

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

The description implies usage for searching by name, state, zip, distance, and degree type, but does not explicitly mention when to use this tool over siblings. It provides clear context but no exclusions or direct references to alternatives, so the guidance is implied rather than explicit.

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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