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nss_scores

Get National Student Survey positivity scores for a university provider, including OfS benchmark and UK sector average comparisons. Filter by subject, question, and study mode.

Instructions

National Student Survey results for one provider.

Returns positivity scores (the % of students answering positively) per
question and theme, with two comparators: the OfS benchmark (the score a
provider with this mix of students and subjects would be expected to get,
including whether the provider is materially above or below it) and the
UK sector average on the same cut. A score without its benchmark is
misleading; always quote both.

Args:
    ukprn: Provider id from search_providers.
    subject: Optional CAH subject name filter, e.g. "Law", "Nursing"
        (matches partially). Omit for provider-level results.
    question: Optional filter on question/theme text, e.g. "feedback",
        "Theme 3", "Q14". Omit for everything.
    subject_level: Granularity of the subject cut when `subject` is given:
        "CAH1" (broadest, ~23 groups), "CAH2" (default, ~35 groups) or
        "CAH3" (finest, ~100+). Without this the same subject would appear
        once per level.
    population: "Registered" (default; the OfS headline convention) or
        "Taught".
    mode_of_study: "All modes" (default), "Full-time", "Part-time" or
        "Apprenticeship".
    level_of_study: "First degree" (default), "Other undergraduate",
        "Undergraduate with postgraduate component" or
        "All undergraduates".

This is survey data about the student experience. It cannot tell you
application volumes, offer rates, entry requirements or fees — none of
that is in the NSS. Say so rather than inferring it from satisfaction.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ukprnYes
subjectNo
questionNo
populationNoRegistered
mode_of_studyNoAll modes
subject_levelNoCAH2
level_of_studyNoFirst degree

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided, so the description fully discloses the survey data nature, the return structure (positivity scores with comparators), and limitations (cannot infer other metrics). This provides comprehensive 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.

Conciseness5/5

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

Efficiently structured with a summary sentence, a detailed Args block, and a concluding note on limitations. Every sentence adds value without waste, and the purpose is front-loaded.

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 7 parameters and an existing output schema, the description covers all parameter semantics, usage guidance, and data limitations. It is fully complete for an agent to understand and invoke the tool correctly.

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?

Schema description coverage is 0%, but the description explains each parameter in detail with defaults, examples, and the effect of combinations (e.g., subject filter with subject_level granularity). This adds significant meaning beyond the 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 it returns National Student Survey results for one provider, including positivity scores per question/theme with benchmarks and UK sector averages. This distinguishes it from sibling tools like b3_outcomes, compare_providers, leo_earnings, and search_providers.

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

Usage Guidelines5/5

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

Explicitly advises that a score without its benchmark is misleading and to always quote both. Also states what the tool cannot provide (application volumes, offer rates, etc.), guiding when not to use it.

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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