One U Education

Get Australian student visa grant rates

get_student_visa_grant_rates

Read-onlyIdempotent

Query published Australian student visa grant and refusal decisions (BP0015) with optional filters, monthly results and a breakdown. Results include the official source, period, snapshot and sample notices. Grant rates describe historical decision groups. This public endpoint returns English natural-language text, preserving sources, conditions and pagination.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_student_visa_grant_rates"
  }
}

Input Schema

Name	Required	Description
`fy`	No	Starting year of the Australian financial year. For example, 2025 means July 2025 to June 2026. Use fy or months.
`age`	No	Age group as reported in the source dataset.
`state`	No	Education provider’s registered state or territory.
`months`	No	Number of months ending at the snapshot’s latest decision month. Defaults to 12 when fy is absent.
`sector`	No	Education sector, such as Higher Education Sector or higher-education.
`country`	No	Country of citizenship. Use an official name, a Chinese or English alias, or an ISO3 code.
`location`	No	Applicant location, In Australia or Outside Australia.
`snapshotId`	No	Optional previously published snapshot ID. Omit for the current snapshot.
`breakdown_by`	No	Optional grouping dimension. The total and monthly series still use all requested filters.
`applicantType`	No	Applicant type, Primary or Secondary.

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 10, 2026

TDQS

A3.6/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), so the description earns credit for adding what they don't: that results carry the official source, period, snapshot and sample notices, that snapshots are pinned objects, and that output is English natural-language text with pagination. The 'historical decision groups' caveat is genuinely useful framing not present in structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

The opening sentence is well front-loaded, but the closing line about returning 'English natural-language text, preserving sources, conditions and pagination' partly repeats the earlier 'Results include the official source, period, snapshot' clause, so not every sentence fully earns its place.

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?

With 10 optional parameters, no output schema and full schema coverage, the description supplies what the structured data cannot: the dataset identity, the historical nature of the metric, snapshot/pagination behavior and output language. Return-shape detail beyond that is legitimately handled by the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and each of the 10 parameters is already described in the schema, including fy/months interplay and country aliases. The description only gestures at filters, monthly results and breakdown without adding format or precedence detail, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb and resource — querying published Australian student visa grant and refusal decisions (BP0015) — plus what comes back (monthly results, breakdown). The dataset code and the grant/refusal framing make it clearly distinct from siblings like get_visa_processing_times or get_visa_fees, though it never explicitly routes against them.

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?

Usage is only implied ('with optional filters') and the one real caveat — that grant rates describe historical decision groups — is a warning about interpretation rather than a when-to-use rule. There is no statement of when to prefer this tool over a sibling or when it is the wrong source, leaving the agent to infer context from the name.

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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