Scholarship database, GPA converter, visa guidance, and cost estimator for African postgraduate students. 172 full-ride + 61 full-tuition scholarships across 32 countries, 54 nationalities, 13 grading systems. 6 tools. No auth required.
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Average 4.1/5 across 6 of 6 tools scored. Lowest: 3.4/5.
Each tool targets a distinct task: visa checking, GPA conversion, cost estimation, scholarship details, personalized matching, and database search. No overlapping purposes.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., check_visa, convert_gpa, search_scholarships).
6 tools is well-scoped for a study abroad planning assistant, covering key functionalities without being overwhelming.
The set covers essential tasks for an African student planning postgraduate study abroad: visa eligibility, grade conversion, cost comparison, and scholarship discovery. No obvious gaps.
check_visaAInspectCheck 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.
| Name | Required | Description | Default |
|---|---|---|---|
| destination | Yes | Destination country. Examples: 'UK', 'Germany', 'USA', 'Canada', 'Australia', 'France', 'Netherlands', 'Sweden', 'Norway', 'Japan', 'China', 'South Korea', 'Italy', 'Austria', 'Portugal'. | |
| nationality | Yes | The student's nationality. Examples: 'Nigerian', 'Kenyan', 'Ghanaian', 'South African', 'Ethiopian', 'Tanzanian', 'Ugandan', 'Rwandan', 'Senegalese', 'Moroccan', 'Egyptian', 'Zambian', 'Zimbabwean', 'Cameroonian'. |
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.
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.
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.
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.
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.
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.
convert_gpaAInspectConvert a grade from any of 13 African grading systems to the international 4.0 GPA scale used by scholarship applications worldwide. Covers all 54 African countries. Also returns the grade class (e.g. 'First Class Honors') and how the GPA is interpreted in Germany, UK, France, USA/Canada, Australia, Netherlands, and Japan/Korea/China.
| Name | Required | Description | Default |
|---|---|---|---|
| grade | Yes | The grade class or value. For class-based systems use: 'first_class', 'second_upper', 'second_lower', 'third_class', 'pass'. For kenyan letter grades: 'A', 'B+', 'B', 'C+', 'C', 'D'. For percentage systems: '75%', '68', etc. For french_20: 'tres_bien', 'bien', 'assez_bien', 'passable'. For spanish_10: 'matricula', 'notable', 'aprobado'. | |
| scale | Yes | Grading system. nigerian=5.0 scale, kenyan/ghanaian/ethiopian/west_african_4=letter/class, south_african=percentage, tanzanian/ugandan=5.0 class, zimbabwean/rwandan/percentage=%, french_20=20-point (26 Francophone+Lusophone countries), spanish_10=10-point (Equatorial Guinea). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses return values including grade class and interpretations across multiple countries. No annotations provided, but description sufficiently covers behavioral aspects for a read-only conversion tool.
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, no filler. First sentence states core action, second adds return details. Efficient and front-loaded.
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 the complexity (13 systems, multiple countries), the description is complete. No output schema, but description explains returns sufficiently.
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 detailed descriptions, and the description adds context like 'Covers all 54 African countries' and examples of grade values. Adds real-world understanding beyond schema.
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?
Clearly states verb 'convert' and resource 'grade' from 13 African grading systems to international 4.0 GPA. Distinguishes from sibling tools which are about visas, costs, 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?
Indicates usage for scholarship applications worldwide. Does not explicitly state when not to use, but siblings are unrelated, making context clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
estimate_study_costsAInspectEstimate and compare the total annual cost of studying in one or more countries for an African postgraduate student. Covers 32 destinations: Tier 3 affordable EU (Germany, France, Italy, Austria, Portugal, Poland, Czech Republic, Estonia, Baltic states, Balkans), Tier 2 full-tuition scholarship countries (Netherlands, Belgium, Scandinavian countries, Japan, South Korea, Switzerland), and benchmark high-cost countries (UK, USA, Canada, Australia). Returns annual cost breakdown: tuition, living, flights, health insurance, visa fee, and total. Use scholarship_mode=true to show the self-funding gap when tuition is already covered by a scholarship.
| Name | Required | Description | Default |
|---|---|---|---|
| lifestyle | No | Budget = shared accommodation, public transport, cooking at home. Comfortable = private room, occasional dining out. Default: budget. | |
| degree_level | No | Degree level. Default: masters. | |
| destinations | No | List of country IDs or names to compare. IDs: germany, france, italy, austria, portugal, poland, czech_republic, estonia, latvia, lithuania, romania, slovakia, croatia, hungary, slovenia, bulgaria, greece, netherlands, belgium, ireland, denmark, finland, sweden, norway, switzerland, japan, south_korea, hong_kong, uk, usa, canada, australia. Alternatively use common names: 'Germany', 'UK', 'United States', etc. Leave empty to return all destinations. | |
| scholarship_mode | No | If true, sets tuition to €0 (assuming tuition is covered by scholarship) and shows only living costs, flights, insurance, and visa. Useful for comparing cities when a student already has a tuition scholarship. Default: false. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses behavior: returns cost breakdown, effect of scholarship_mode, and covers 32 destinations. It does not mention limitations or data freshness, but is fairly transparent for a cost estimation tool.
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 a single dense paragraph but effectively front-loaded with core purpose. It could be more structured (e.g., bullet points), but it's not overly long and each sentence adds value.
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 4 parameters and no output schema, the description thoroughly explains what the tool does, each parameter's effect, and the return breakdown. It covers all necessary aspects for an agent to 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 coverage is 100%, so baseline is 3. The description adds meaning beyond schema: for destinations, lists IDs and common names; for lifestyle, explains cost implications; for scholarship_mode, clarifies tuition set to €0 and output components. This adds significant 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 clearly states the tool's purpose: estimate and compare total annual cost of studying in multiple countries for African postgraduate students. It lists covered destinations and return breakdown, distinguishing it from siblings like check_visa 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 provides guidance on using scholarship_mode to show self-funding gap. However, it does not explicitly state when to use this tool over siblings or when not to use it, though the context makes it reasonably clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_scholarshipBInspectGet complete details on a specific scholarship by name or keyword search. Returns: full name, host country, scholarship amount, deadline, minimum GPA, essay type, acceptance rate, visa accessibility, partner universities, application URL, and committee values.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Scholarship name or search keyword. Examples: 'Chevening', 'DAAD', 'Rhodes', 'Fulbright', 'Gates Cambridge', 'Commonwealth', 'Erasmus Mundus', 'MasterCard Foundation', 'Aga Khan', 'Australia Awards', 'MEXT', 'Korea GKS', 'Eiffel'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only lists return values but does not state that the tool is read-only, has no side effects, or any other behaviors. The read-only nature is implied but not explicit.
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 concise sentences: first states purpose and mode, second enumerates return fields. No superfluous words; front-loaded with purpose.
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 parameter set (1 param, no output schema), the description adequately explains what the tool does and returns. However, it lacks guidance on when to use versus siblings (search_scholarships), so slightly incomplete.
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 a well-described 'name' parameter including examples. The description adds 'by name or keyword search' and a list of return fields, which provides context beyond the schema but does not enhance the parameter itself significantly.
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 verb 'get' and resource 'scholarship details', specifying search by name or keyword. It lists the return fields, distinguishing it clearly from siblings like search_scholarships (list) or match_scholarships (matching).
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?
No guidance on when to use this tool versus alternatives (e.g., use search_scholarships for a list, get_scholarship for full details). No when-not-to-use conditions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
match_scholarshipsAInspectGet personalised, ranked scholarship recommendations for an African student based on their full profile. Uses StudiePoint's matching algorithm to score scholarships by GPA fit, field alignment, visa accessibility, deadline urgency, and acceptance rate. Returns top matches with match score, reasoning, and direct application links.
| Name | Required | Description | Default |
|---|---|---|---|
| field | Yes | Student's field of study. | |
| limit | No | Number of top matches to return. Default 5, max 15. | |
| nationality | Yes | Student's nationality (used for visa scoring). | |
| degree_level | No | Degree level sought. | |
| converted_gpa | Yes | Student's GPA already converted to the 4.0 scale. Use convert_gpa first if needed. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden. It discloses the matching algorithm and scoring factors, and states the return includes match score, reasoning, and links. However, it does not mention limitations, data freshness, or any side effects. A score of 3 reflects adequate but not exhaustive 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 highly concise: two sentences covering purpose, methodology, and output. It front-loads the main action and avoids unnecessary details, making it efficient for an AI agent.
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 the complexity of a matching tool and the absence of an output schema, the description covers the core functionality well: what it does, how it scores, and what it returns. Minor gaps include no mention of error handling or assumptions about data completeness, but it is largely complete for its purpose.
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 baseline is 3. The description adds value by connecting parameters to the algorithm (e.g., field alignment, visa impact) and indicating that 'converted_gpa' should be pre-processed with 'convert_gpa'. This enriches understanding beyond schema alone.
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 purpose: providing personalized, ranked scholarship recommendations for African students. It specifies the algorithm factors (GPA fit, field alignment, visa accessibility, etc.) and output format (match score, reasoning, links). This effectively distinguishes it from the sibling 'search_scholarships', which likely performs a general search.
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 when a full student profile is available, and explicitly directs users to 'convert_gpa' first if needed, linking to a sibling tool. However, it does not explicitly state when to use this tool versus alternatives like 'search_scholarships' or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_scholarshipsAInspectSearch StudiePoint's database of 200+ full-ride scholarships and 100+ full-tuition scholarships for African postgraduate students. Filter by destination country, field of study, minimum GPA (4.0 scale), degree level, and tier. Returns scholarship name, country, amount, deadline, minimum GPA, essay type, visa accessibility, and application URL.
| Name | Required | Description | Default |
|---|---|---|---|
| tier | No | 'full_ride' = fully funded (tuition + living + flights). 'full_tuition' = tuition only, student funds living. 'affordable_eu' = low/no-tuition EU universities across 17 countries. | |
| field | No | Field of study. Examples: 'engineering', 'medicine', 'business', 'law', 'sciences', 'arts', 'agriculture', 'public health', 'economics', 'computer science'. | |
| country | No | Destination country. Examples: 'UK', 'Germany', 'USA', 'Canada', 'Australia', 'Netherlands', 'France', 'Japan', 'China', 'South Korea', 'Sweden', 'Norway'. | |
| min_gpa | No | Maximum minimum GPA required (4.0 scale). Only return scholarships the student can qualify for. | |
| degree_level | No | Degree level: 'masters' or 'phd'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses what the tool does and returns, but does not mention behavioral aspects like authentication needs, rate limits, or whether the search is real-time or cached. For a search tool with no annotations, this is adequate but not exceptional, resulting in a 3.
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: first sentence states purpose and scope, second lists output fields. Every word contributes value; there is no redundancy or fluff. This efficiency earns a 5.
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 compensates by listing return fields (scholarship name, country, amount, etc.). It covers the database size, target audience, all filter criteria, and output details. For a search tool with 5 parameters, this is fully complete, earning a 5.
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 schema already documents each parameter's meaning. The tool description adds context about the database size and target audience but does not enhance parameter semantics beyond what the schema provides. Baseline 3 is earned.
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 searches a specific database (StudiePoint's) of 200+ full-ride and 100+ full-tuition scholarships for African postgraduate students. It uses precise verbs and resource identification, and the scope is distinct from sibling tools like get_scholarship (single record retrieval) and match_scholarships (matching algorithm), earning a 5.
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 makes it obvious this tool is for searching/filtering scholarships by various criteria. However, it does not explicitly state when to use this tool over siblings (e.g., when to use convert_gpa or estimate_study_costs instead), lacking 'when-not' guidance. The context is clear but not exhaustive, so a 4 is appropriate.
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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