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StudiePoint AI

by ai.studiepoint

check_visa

Check the visa accessibility rating for an African student of a given nationality applying to study in a specific destination country. Returns a rating (green = easy, yellow = moderate, red = difficult/restricted), notes on the visa process, and any important restrictions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
destinationYesDestination country. Examples: 'UK', 'Germany', 'USA', 'Canada', 'Australia', 'France', 'Netherlands', 'Sweden', 'Norway', 'Japan', 'China', 'South Korea', 'Italy', 'Austria', 'Portugal'.
nationalityYesThe student's nationality. Examples: 'Nigerian', 'Kenyan', 'Ghanaian', 'South African', 'Ethiopian', 'Tanzanian', 'Ugandan', 'Rwandan', 'Senegalese', 'Moroccan', 'Egyptian', 'Zambian', 'Zimbabwean', 'Cameroonian'.

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description must carry the burden. It describes the return value (rating, notes, restrictions) but does not disclose any potential side effects, required permissions, rate limits, or behavior on invalid inputs. It is adequate but lacks depth.

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 two sentences, front-loading the core purpose and then detailing the return values. Every sentence adds value without redundancy or unnecessary elaboration.

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?

Given the tool's simplicity (2 parameters, no output schema, no nested objects), the description covers the essential what and why. However, it could include more detail on the output structure or examples to fully compensate for the lack of an output 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 description coverage is 100%, with both parameters well-described in the schema. The description adds no new information about parameters beyond what the schema provides, so a baseline score of 3 is appropriate.

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 identifies the verb 'check' and the resource 'visa accessibility rating', specifies the input (nationality and destination) and output (rating, notes, restrictions). It effectively distinguishes this tool from siblings like 'convert_gpa' or 'search_scholarships' which deal with other aspects of studying abroad.

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 states the tool is for 'an African student' implying a specific use case. Without explicit when-to-use or when-not-to-use, the context is clear enough given sibling tools are about scholarships and costs. No exclusions or alternatives are mentioned, but the purpose is distinct.

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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TDQS

A4.3/5.0
Disambiguation5/5

Each tool serves a distinct function: visa checks, GPA conversion, cost estimation, scholarship lookup (specific), matching, and searching. There is clear separation between search and get/match, and between cost, visa, and GPA utilities.

Naming Consistency5/5

All tools use a clear verb_noun pattern: check_visa, convert_gpa, estimate_study_costs, get_scholarship, match_scholarships, search_scholarships. The verbs accurately describe the action, and nouns are the domain objects. Perfect consistency.

Tool Count5/5

Six tools is an ideal size for a specialized education/scholarship assistant. Each tool covers a core need without redundancy, and the count is well within the sweet spot for agent usability.

Completeness4/5

The toolset covers the core pre-application workflow: checking visa feasibility, converting GPA, estimating costs, and finding/matching scholarships. The main gap is lack of direct scholarship application or document submission tools, but those are likely outside the server's scope (it's a research/planning assistant).

Resources