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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does well: it specifies the exact return breakdown (tuition, living, flights, health insurance, visa fee, total) and explains the scholarship_mode behavior ('show the self-funding gap when tuition is already covered by a scholarship'). Minor gaps remain — currency, data vintage, and estimation assumptions are not disclosed — but the core behavior is transparent.

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 front-loaded with its core purpose, then proceeds logically through scope, output format, and special mode. The destination enumeration is long but earns its place by giving the agent concrete valid values. Every sentence contributes; nothing is wasted.

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?

Given there is no output schema and no annotations, the description carries substantial burden — and it largely delivers: return format, destination scope, target audience, and the scholarship_mode toggle are all explained. The remaining gaps (currency of the cost figures, underlying assumptions or data vintage) are minor for a cost-estimation tool but would help an agent set user expectations.

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 modest value beyond the schema: it frames scholarship_mode as revealing the 'self-funding gap' and categorizes destinations into tiers, which complements the schema's flat ID list. It adds nothing for lifestyle or degree_level, which the schema already documents adequately.

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 opens with a specific verb and resource: 'Estimate and compare the total annual cost of studying in one or more countries for an African postgraduate student.' It names the audience, the scope (32 destinations across three tiers), and the output (annual cost breakdown). This clearly distinguishes it from sibling tools focused on visas, GPA conversion, 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 purpose strongly implies when to use the tool (cost estimation/comparison questions), and the siblings are functionally distinct enough that confusion is unlikely. However, the description never explicitly states when not to use it or points to alternatives — notably, scholarship_mode overlaps conceptually with the scholarship-related siblings (get_scholarship, match_scholarships, search_scholarships), and that boundary is never addressed.

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 bears full responsibility for behavioral disclosure. It clearly states the tool returns essential public facts and sets an implicit scope by contrasting with the private workflow, which suggests this tool does not provide private coaching or detailed guidance. It does not mention potential null results or edge cases, but the read-only nature is implied by 'look up.'

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 with no filler. The core purpose is front-loaded, and the redirect to the private workflow is a concise, valuable addition. Every word 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?

For a simple single-parameter lookup tool with no output schema, the description fully covers what the tool does and what it returns. It lists the fields returned and implicitly sets expectations about scope, making it complete for an agent to call 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?

The input schema fully describes the single 'name' parameter with examples and coverage at 100%, so the description adds little semantic value beyond what the schema already provides. The description does not elaborate on the parameter's format or usage beyond the schema, which is adequate given the high schema coverage.

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 'look up' with a clear resource 'named scholarship' and enumerates the exact facts returned (host country, programme, eligible fields, amount, deadline, minimum GPA, link). This distinguishes it from sibling tools like search_scholarships (which searches broadly) and match_scholarships (which matches).

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 directs users to a different workflow ('StudiePoint's private student workflow') for essay coaching, fit guidance, and application planning, clearly indicating when NOT to use this tool. However, it does not directly compare against sibling tools like search_scholarships or match_scholarships, leaving their distinction implicit rather than explicit.

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 stating that the tool uses StudiePoint's matching algorithm and scoring criteria (GPA fit, field alignment, visa accessibility, deadline urgency, acceptance rate) and explicitly describes the output (match score, reasoning, direct application links). It does not mention side effects, errors, or rate limits, but for a non-destructive recommendation tool this is sufficient 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 efficient and front-loaded: the first sentence states the core purpose, the second explains the algorithm, and the third covers the output. Every sentence earns its place, with zero fluff or repetition of schema details. This is a model of concise, structured writing.

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 tool with 5 parameters, no annotations, and no output schema, the description is remarkably complete. It states the target user, the input basis, the scoring logic, and the exact return content (match score, reasoning, application links). The schema covers parameter specifics like defaults and enum values, so nothing an agent needs to call the tool correctly is missing.

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 describes all 5 parameters with 100% coverage, so the baseline is 3. The tool description adds some connective meaning by tying parameters to algorithm criteria (e.g., 'GPA fit' and 'field alignment' map to converted_gpa and field), but it does not provide new per-parameter details beyond what the schema gives. It also says nothing about the limit parameter, leaving the schema to do the heavy lifting.

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 opens with a specific verb and resource: 'Get personalised, ranked scholarship recommendations for an African student based on their full profile.' It clearly defines the tool's purpose and differentiates it from siblings like search_scholarships (which implies plain search) and get_scholarship (which implies retrieval of a single scholarship). The mention of StudiePoint's matching algorithm and output structure further pins down what makes this tool distinct.

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 — use this tool when you need personalised, ranked recommendations for an African student, not just a search or single-scholarship lookup. It implies the right scenario through phrases like 'personalised,' 'ranked,' and 'based on their full profile.' However, it does not explicitly name alternative tools or state when not to use this tool, which keeps it slightly below the explicit standard.

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.