StudiePoint AI

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

With no annotations provided, the description carries full burden. It discloses return values (rating, notes, restrictions) and suggests a read-only operation via 'Check', but it does not mention any side effects, dependencies, or limitations (e.g., unsupported nationalities/destinations). This is moderate transparency but lacks edge-case behavior.

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

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

The description is two sentences, front-loaded with the main purpose, then details output. Every sentence adds meaning and there is zero redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple 2-parameter tool without output schema, the description covers inputs, outputs, and even the rating color scheme. It does not explicitly address unsupported inputs or fallback behavior, which would be useful but not critical given the examples provided.

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

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

Schema description coverage is 100% for both parameters, each with examples and descriptions. The tool description adds no further parameter detail beyond what the schema already provides, so the baseline 3 applies.

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

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

The description explicitly states the tool checks visa accessibility rating for a specific African student nationality and destination, which is a distinct resource from sibling tools about GPA, costs, and scholarships. The verb 'Check' and specific output (rating, notes, restrictions) make the purpose unambiguous.

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

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

The description implies usage for visa assessment queries but does not explicitly state when to use this tool over alternatives or exclude other tools. There are no competing visa tools among siblings, so context is clear but without explicit when/when-not guidance, it falls to implied usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the full burden. It discloses that the tool also returns grade class and international interpretations, giving concrete behavioral expectations beyond the simple conversion. It does not mention error handling, but for a pure conversion tool this is adequate.

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

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

The description is two sentences, front-loaded with the primary action and immediately followed by additional return information. Every clause earns its place with no redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has a simple footprint (2 params, no output schema), and the description compensates by stating what is returned (grade class, international interpretations) and the intended use (scholarship applications). This is complete for an agent to select and invoke correctly.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds no parameter-specific meaning beyond what is already in the schema; it only reinforces the tool's overall scope (13 systems, 54 countries). No further compensation needed.

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

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

The description uses a specific verb ('Convert') and resource ('grade from any of 13 African grading systems to the international 4.0 GPA scale'), clearly distinguishing it from sibling scholarship, visa, and cost tools. It also notes coverage of all 54 African countries, adding scope.

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

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

The description explicitly ties usage to scholarship applications worldwide, providing clear context for when to use the tool. It does not explicitly name alternatives or exclusions, but the sibling tools are sufficiently different that no confusion exists.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description must carry the transparency burden. It discloses the return structure (tuition, living, flights, health insurance, visa fee, total), but it does not mention underlying assumptions, data sources, or limitations (e.g., whether costs are current, currency, or how estimates are derived). This is adequate but not rich.

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

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

The description is three sentences with no fluff. It front-loads the purpose, lists coverage, and provides return breakdown and a usage tip, all in a compact structure that earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is moderately complex with multiple destinations and modes. The description covers the cost components, destination tiers, and scholarship mode. With no output schema, it explains the return values sufficiently. Minor omissions (e.g., currency, data currentness) prevent a perfect score, but it is complete enough for most agents.

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

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

Schema coverage is 100%, so the schema already describes all parameters. The description adds value only for scholarship_mode by explaining its purpose ('show the self-funding gap'), while other parameters are not elaborated beyond the schema. This meets the baseline of 3, though the extra commentary on scholarship_mode gives slight incremental value.

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

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

States precisely what the tool does: 'Estimate and compare the total annual cost of studying in one or more countries for an African postgraduate student.' It specifies the resource (costs), the verb (estimate and compare), and the scope (32 destinations, African postgraduate students). It clearly distinguishes itself from siblings focused on visas, GPA, and scholarships.

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

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

The description gives clear context on the tool's use case and even introduces scholarship_mode as a conditional use ('Use scholarship_mode=true to show the self-funding gap'). However, it does not explicitly mention alternatives or state when not to use this tool versus siblings, so it falls short of a full 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the transparency burden. It lists return data (e.g., amount, deadline, GPA), which is useful, but it does not disclose error behavior, ambiguous keyword handling, or explicitly state the operation is read-only.

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

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

Two sentences, front-loaded with purpose, followed by a concise list of return fields. Every word earns its place, with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple single-parameter tool with no output schema or annotations, the description adequately covers what it does, how to invoke it, and what to expect in the response. It could mention not-found behavior, but overall it is complete for its complexity.

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

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

The input schema already documents the 'name' parameter thoroughly with examples, achieving 100% schema coverage. The description adds no additional parameter semantics beyond what the schema provides, so baseline 3 applies.

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

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

The description uses a specific verb 'Get' and resource 'complete details on a specific scholarship', clearly distinguishing it from search_scholarships or match_scholarships. It enumerates exact return fields, making the tool's function unambiguous.

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

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

The description implies usage for retrieving full details on a known scholarship via name or keyword. It does not explicitly reference alternatives or exclusions, but the sibling tool names provide context. Lacks explicit when-not guidance, so not a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by revealing the scoring criteria (GPA fit, field alignment, visa accessibility, deadline urgency, acceptance rate) and the output structure (match score, reasoning, direct links). It doesn't mention limitations or data sources, but for a non-mutating recommender this is solid transparency.

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

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

The description is two sentences, front-loaded with the core action, and each sentence adds value: the first defines the tool's purpose, the second explains the algorithm and output. There is no fluff or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description rightly explains return values ('top matches with match score, reasoning, and direct application links'). It also covers the algorithm and inputs, making it largely complete for a recommendation tool. It could mention edge cases (e.g., no matches) but that is not essential given the output description.

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

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

The schema covers all parameters with descriptions (100% coverage), so the baseline is 3. The description adds some context by linking parameters to the algorithm (e.g., nationality for visa access, converted_gpa for GPA fit), but this is marginal beyond the schema's own field descriptions. It does not introduce new parameter format or syntax details.

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

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

The description clearly states the tool's function: 'Get personalised, ranked scholarship recommendations for an African student based on their full profile.' It specifies the resource (scholarships), the action (matching/ranking), and the target audience, distinguishing it from siblings like search_scholarships or get_scholarship.

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

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

The description implies when to use this tool (when personalized, ranked recommendations are needed) and provides context through its algorithm explanation, but it does not explicitly state alternatives or exclusion criteria such as 'use get_scholarship for a specific scholarship.' Clear usage context is present, but no explicit when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

The description discloses output fields returned (name, country, amount, deadline, etc.) and clarifies the GPA scale (4.0), which adds value beyond the schema. However, it does not mention pagination, default behavior with no filters, or filter semantics (e.g., AND/OR), leaving some behavioral ambiguity for a tool with no annotations.

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

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

The description is three sentences, front-loaded with the action and scope, then lists filters and outputs efficiently. No redundant or irrelevant content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description adequately covers the tool's purpose, filterable parameters, and return fields for an agent to use it effectively. Since the schema already documents parameters and there is no output schema, the return field list compensates. Minor gaps like default behavior or result limits are not critical for a search tool.

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

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

Schema coverage is 100%, with each parameter having a rich description including examples and enumerations. The tool description only restates the filter names without adding new semantic meaning, so it meets the baseline but adds no extra value.

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

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

The description explicitly states the tool searches a scholarship database, specifies the target audience (African postgraduate students), and lists filterable criteria (country, field, GPA, degree level, tier). It clearly distinguishes from siblings like get_scholarship by framing this as a search operation.

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

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

The description conveys the tool's purpose of searching and filtering scholarships, implying it should be used when a student needs to find scholarships matching specific criteria. However, it does not explicitly name alternative tools or state when to prefer match_scholarships or get_scholarship, so it is clear but lacks exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.