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compare_providers

Compare NSS results across multiple higher education providers side by side, using OfS benchmarks to see which exceed expectations.

Instructions

Compare NSS results across two or more providers, side by side.

Returns one entry per question/theme, each holding every provider's
positivity score, its OfS benchmark and its position against it, plus the
UK sector average for context. Because each provider is judged against
its own benchmark, this comparison is fairer than ranking raw scores —
say which providers beat their benchmark, not just who scored highest.

Args:
    ukprns: Two or more provider ids from search_providers.
    subject: Optional CAH subject name filter, e.g. "Computing". Omit to
        compare at whole-provider level.
    question: Filter on question/theme text. Defaults to "Theme" (the
        seven headline themes), which keeps the comparison readable.
        Pass None (or a specific question) to widen or narrow it.
    subject_level, population, mode_of_study, level_of_study: As in
        nss_scores.

A provider missing from a row has suppressed data on that cut (under 10
respondents) — report the gap, don't fill it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ukprnsYes
subjectNo
questionNoTheme
populationNoRegistered
mode_of_studyNoAll modes
subject_levelNoCAH2
level_of_studyNoFirst degree

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Discloses that missing providers indicate suppressed data (under 10 respondents) and that output includes positivity scores, benchmarks, and UK averages. No annotations exist, so description carries full burden.

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?

Front-loaded with main purpose, followed by output explanation, parameter details, and a specific behavior note. No wasted words.

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

Completeness4/5

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

Covers output structure and suppression behavior, but relies on referencing nss_scores for some parameters, which may not be fully self-contained.

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?

Describes key parameters (ukprns as provider ids from search_providers, subject as CAH filter, question as theme filter) but defers subject_level, population, etc. to 'As in nss_scores', assuming familiarity.

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?

Clearly states the tool compares NSS results across providers, distinguishing it from search_providers and nss_scores by focusing on side-by-side comparison.

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?

Explains that the comparison is fairer than raw ranking and provides parameter guidance (e.g., default question is 'Theme'), but does not explicitly state when to use alternatives like nss_scores.

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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