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wilgreeves-cmd

uk-he-data-mcp

by wilgreeves-cmd
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b3_outcomes

Retrieve student continuation, completion, and progression rates for a UK higher education provider, with OfS benchmarks and regulatory thresholds. Filter by subject, demographics, mode, or level.

Instructions

Student outcomes (OfS B3 indicators) for one provider.

Three indicators, each a percentage of students:
  - Continuation: still in higher education (or qualified) about a year
    after starting.
  - Completion: qualified or still studying four years after starting.
  - Progression: in managerial/professional employment or further study
    15 months after graduating.

Each row carries the OfS benchmark (expected value given the provider's
student and course mix), the regulatory minimum threshold the OfS
enforces (condition B3), and the number of students behind the figure.
Quote outcomes against the benchmark, not just the raw percentage. Years
are pooled windows (e.g. "2019-2022") — the latest available per
indicator is returned.

Args:
    ukprn: Provider id from search_providers.
    indicator: Optional filter — "Continuation", "Completion" or
        "Progression" (partial match). Omit for all three.
    subject: Optional subject filter by CAH name ("Law") or code
        ("CAH16-01"). Omit for whole-provider figures.
    split_type: Optional demographic split instead of subject — e.g.
        "Sex", "Ethnicity", "AgeOnCommencement", "Disability",
        "DeprivationQuintile". Use with or without split_value.
    split_value: Optional value within the split, e.g. "Female",
        "Mature". Partial match.
    mode: "Full-time" (default), "Part-time" or "Apprenticeship".
    level: "FirstDegree" (default), "AllUndergraduates",
        "OtherUndergraduate", "PostgraduateTaughtMasters",
        "PostgraduateResearch", "PGCE" and others.

A NULL value with a supp_reason (e.g. "[low]") means the OfS suppressed
that figure, usually for small numbers — report the suppression, don't
estimate. This dataset has no application volumes, entry grades or
satisfaction scores (NSS tools cover satisfaction).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoFull-time
levelNoFirstDegree
ukprnYes
subjectNo
indicatorNo
split_typeNo
split_valueNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.8/5.0
Behavior5/5

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

No annotations are provided, so the description fully bears the burden of transparency. It explains data source (OfS), what each indicator represents, benchmark and threshold inclusion, pooled years, latest data retrieval, handling of NULL values with suppression reasons, and exclusions (no application volumes, entry grades, or satisfaction scores).

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 summary, bullet-pointed indicator details, and clear argument list. It is front-loaded with the core purpose and each sentence adds distinct value without redundancy.

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 (7 parameters, output schema exists), the description covers all necessary aspects: return structure, parameter usage, suppression handling, and data limitations. It is comprehensive enough for an AI agent to effectively use the tool.

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 thoroughly explains all 7 parameters, including default values, allowed values, examples, and relationships (e.g., split_type and split_value). It also provides context for ukprn (from search_providers) and subject (CAH name or code).

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 returns OfS B3 student outcomes for one provider, listing the three indicators and explaining their meaning. It implicitly distinguishes from siblings like compare_providers and nss_scores by specifying that it is for a single provider and noting that satisfaction scores are covered by NSS tools.

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 provides explicit guidance on when to use the tool (e.g., for a specific provider) and details on filtering, but does not explicitly state when not to use it or compare directly to sibling tools. However, it does mention that satisfaction scores are not included, directing users to NSS tools.

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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