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.
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.
| 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 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 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. Provide one to three 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 the full burden. It discloses the principal behavior by listing the returned cost components and explains the effect of scholarship_mode on the output. For a read-only estimation tool, this is strong transparency, though it could ideally mention data sources or assumptions.
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 dense sentences with the purpose front-loaded and no filler. The destination tier list is useful though partially redundant with the schema's enum names. Overall, it is concise and well-structured.
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?
Since there is no output schema, the description correctly lists the return fields (tuition, living, flights, health insurance, visa fee, total). It also covers the target audience, destination scope, and scholarship_mode. A minor gap is the lack of details about data sources or currency, but the description is sufficient for a 4-parameter tool with full schema coverage.
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% and each parameter already has a detailed description. The main description adds marginal value by grouping destinations into tiers and clarifying that scholarship_mode reveals the self-funding gap, but the schema already covers the behavioral details. Thus, baseline 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 opens with a specific verb phrase 'Estimate and compare the total annual cost of studying' and clearly defines the target audience ('African postgraduate student') and scope ('one or more countries'). This clearly differentiates it from sibling tools like search_scholarships or check_visa, which focus on different aspects of study planning.
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 clear context for when the tool is useful, such as comparing countries or using scholarship_mode=true to show the self-funding gap. It does not explicitly contrast with alternatives, but the sibling tools' purposes (visa, GPA, scholarships) make the cost-estimation use case unambiguous; lacking explicit 'when not to use' guidance prevents a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Look up essential public facts about a named scholarship: host country, programme, eligible fields, amount, deadline, minimum GPA, and official application link. Use StudiePoint's private student workflow for essay coaching, detailed fit guidance, and application planning.
| 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 carry the burden. It describes the tool as a 'look up' of public facts, implying a read-only operation, but does not explicitly state non-destructive behavior, error handling, authentication needs, or what happens if the scholarship is not found. It does list the output fields, giving some transparency, but lacks depth on behavioral edge cases.
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: the first states the purpose and lists the facts, the second redirects to a private workflow for other needs. It is concise, front-loaded, and every sentence earns its place with no 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?
Given the tool's simplicity (one parameter, no output schema), the description adequately covers the returned fields and clarifies scope. It lacks mention of not-found behavior or auth, but for a simple lookup, these are likely not critical. The description is sufficient for an agent to use the tool correctly in most cases.
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 the phrase 'named scholarship', which clarifies that the 'name' parameter expects a specific scholarship name rather than a broader keyword, even though the schema says 'name or search keyword'. This slight clarification adds value beyond the 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?
The description clearly states the tool's purpose: 'Look up essential public facts about a named scholarship' and lists the specific fields (host country, programme, eligible fields, amount, deadline, minimum GPA, official link). This is a specific verb+resource+scope that distinguishes it from sibling tools like search_scholarships or match_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 explicitly points to a separate private workflow for coaching and planning, but does not contrast with the sibling MCP tools (search/match). It implies usage for a named scholarship lookup, but lacks explicit guidance on when to use this vs. discovery tools. It is not misleading, but could be more explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get 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 3, maximum 3 in the public preview. | |
| 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 carries the full burden and does well: it discloses the scoring factors (GPA fit, field alignment, visa accessibility, deadline urgency, acceptance rate) and return contents (match score, reasoning, application links). It does not state whether the operation is read-only or mention limits, but the algorithm and output behavior are meaningfully described.
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 the primary action, and every clause earns its place. It includes audience, algorithm, scoring criteria, and output without any wasted words.
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 no output schema, so the description compensates by naming the return format (top matches, match score, reasoning, application links). Inputs are well covered by the schema and the description. It could add explicit exclusions or rate-limit notes, but for this complexity it is sufficiently complete.
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 explaining how parameters map to algorithm criteria, e.g. nationality drives 'visa accessibility' and field drives 'field alignment.' It also benefits from the schema's converted_gpa hint to 'use convert_gpa first,' which adds cross-tool semantic meaning.
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 a specific verb and resource: 'Get personalised, ranked scholarship recommendations for an African student based on their full profile.' It further distinguishes the tool from generic search by naming the matching algorithm and ranking criteria, so an agent can easily differentiate it from siblings like search_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?
It clearly defines the appropriate use case: personalized matching for African students with a full profile. However, it does not explicitly state when NOT to use it or mention alternatives by name, such as 'use search_scholarships for filtered lookup.' This is clear context but lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search 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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
estimate_study_costs1 field changedInput schema / properties / destinations / descriptionPrevious value: -"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."New value: +"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. Provide one to three destinations."match_scholarships1 field changedInput schema / properties / limit / descriptionPrevious value: -"Number of top matches to return. Default 5, max 15."New value: +"Number of top matches to return. Default 3, maximum 3 in the public preview."search_scholarships1 field changedInput schema / properties / limitRemoved value: -{
- "description": "Maximum number of results to return. Default 10, max 30.",
- "type": "number"
-}check_visaconvert_gpaestimate_study_costsget_scholarshipmatch_scholarshipssearch_scholarshipsClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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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.
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.
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.
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).