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leo_earnings

Retrieve graduate earnings data for a UK provider, including lower-quartile, median and upper-quartile earnings at 1, 3 and 5 years, by subject, with UK-wide comparison.

Instructions

Graduate earnings (DfE Longitudinal Education Outcomes) for one provider.

Returns lower-quartile, median and upper-quartile annualised earnings at
1, 3 and 5 years after graduation, by CAH2 subject, with the UK-wide
figure on the same cut for context. Always quote the median with its
quartiles — the spread is usually the story — and say how many graduates
the figure is based on.

Args:
    ukprn: Provider id from search_providers.
    subject: Optional CAH2 subject name filter, e.g. "Law", "Computing"
        (partial match). Omit for all subjects.
    years_after: 1, 3 or 5 (years after graduation). Omit for all three.
    tax_year: e.g. "2022/2023". Omit for the latest available.
    characteristic_type: "All graduates" (default), or a split:
        "sex", "ethnicity", "POLAR4", "prior_attainment_code".
    characteristic_value: Value within the split, e.g. "F", "M".

Honest limits, worth repeating to the user: these are pre-tax earnings of
UK graduates in sustained employment whose records matched tax data —
the self-employed abroad, or those out of work, aren't in the medians.
Earnings reflect where graduates live and work (London weighting is
real), and nothing here is causal: a subject's high earnings may be who
it admits, not what it teaches. No figures on application volumes,
satisfaction or dropout — other tools cover the latter two.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ukprnYes
subjectNo
tax_yearNo
years_afterNo
characteristic_typeNoAll graduates
characteristic_valueNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: earnings are pre-tax, from matched tax data, exclude self-employed/unemployed graduates, reflect location weighting, and are not causal. It also states what the tool does not provide (application volumes, satisfaction, dropout). This is comprehensive and honest.

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 with paragraphs for purpose, args, and limitations. It is front-loaded with essential info. While slightly lengthy, every sentence adds value; minor redundancy in repeating 'omit' for parameters could be tightened, but overall efficient.

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 parameters, detailed output) and presence of an output schema, the description covers return values, limitations, and usage context thoroughly. It explains what the data represents and its caveats, making it complete for an agent to invoke 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 coverage is 0%, so description bears full burden. It explains each parameter: ukprn (from search_providers), subject (CAH2 partial match, optional), years_after (1,3,5), tax_year (format example), characteristic_type (default and split options), characteristic_value (value examples). 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 that the tool returns graduate earnings data for one provider, specifying lower-quartile, median, and upper-quartile annualised earnings at 1, 3, and 5 years by CAH2 subject, with UK-wide context. This is distinct from sibling tools like b3_outcomes (broader outcomes) or nss_scores (satisfaction), so purpose is specific and well-differentiated.

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 guides the agent to always quote median with quartiles and mention graduate counts. Discusses honest limits (pre-tax, excludes self-employed, not causal) and clarifies that other tools cover application volumes and dropout. References search_providers for ukprn, providing clear when-to-use and when-not-to-use context.

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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