F
licenseA
qualityB
maintenanceSearches US colleges and scholarships using government data, enabling comparisons of tuition, debt, earnings, and program-specific outcomes.
4
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AdmitBase Admissions
Server Details
Admissions data & match scores for law, medical, dental, MBA, pharmacy, vet, optometry schools
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
3 toolscalculate_match_scoreCalculate match scoreAInspect
Given a user's GPA and test score, calculate match category (Safety/Target/Reach/Far Reach), admission probability, and percentiles for one school or a ranked list. Public — no authentication required.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| gpa | No | Cumulative GPA on a 4.0 scale (omit for ATAR-based Australian schools) | |
| atar | No | Australian Tertiary Admission Rank (0-99.95). Used instead of test_score/gpa for Australian schools, which select on a published ATAR cutoff. | |
| limit | No | Number of top schools to return when no school_slug given | |
| program | Yes | Program type | |
| test_score | No | Test score: LSAT 120-180, MCAT 472-528, DAT 1-30, OAT 200-400, GMAT/GRE for MBA. Omit for pharmacy (PCAT retired 2024 — GPA-only) and for ATAR-based Australian schools. | |
| school_slug | No | If provided, calculate for this school only. Otherwise returns a ranked list. | |
| work_experience_years | No | Years of full-time work experience (MBA programs only) |
TDQS
A4.2/5.0
Behavior4/5
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 discloses the authentication requirement (none) and specifies behavior (returns categories, probability, percentiles; operates for one school or ranked list). It does not state side effects (implicitly none), but the tool's read-only nature is evident from its purpose.
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 one sentence, front-loaded with the action and outcome, with no superfluous words. It efficiently covers purpose, scope, and auth in under 30 words.
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?
The description covers purpose, scope, authentication, and outputs, which is sufficient given the schema's rich parameter coverage. A minor gap is that it doesn't explicitly mention how parameters combine (e.g., ATAR vs GPA), but the schema handles this, so the description is adequately complete.
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%, so the baseline is 3. The description mentions "GPA and test score" but does not add new meaning beyond the schema's detailed parameter descriptions (e.g., ATAR vs GPA, program-specific test ranges). It does not clarify ambiguities or add syntax/format guidance.
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 states the tool's function: "calculate match category (Safety/Target/Reach/Far Reach), admission probability, and percentiles" for "one school or a ranked list." It specifies the verb (calculate), resource (match score), and outputs, distinguishing it from siblings like search_schools or compare_to_applicants.
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 provides clear context on when to use the tool (given GPA/test score) and explicitly notes "Public — no authentication required," which signals accessibility. However, it does not explicitly contrast with alternatives (e.g., compare_to_applicants) or mention when not to use it, leaving some implicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_school_statsGet detailed school statsAInspect
Get full admissions statistics for a specific school: GPA/test percentiles, acceptance rate, class size, tuition, employment outcomes. Public — no authentication required.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| school_name | No | School name to search for (partial match). Used when slug is unknown. | |
| school_slug | No | School slug (e.g. "harvard-law-school"). Use search_schools to find slugs. |
TDQS
A4.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It explicitly states 'Public — no authentication required,' addressing a key behavioral trait. It also lists the type of data returned, setting expectations. It doesn't mention error handling or output format, but for a simple read-only getter, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence: 'Get full admissions statistics for a specific school: ...' It efficiently conveys the verb, resource, and scope without redundancy or filler.
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?
The tool is simple with two optional parameters, both thoroughly documented in the schema. The description adds the public-access trait and lists the stats returned, compensating for the lack of an output schema. It doesn't explain which parameter to supply, but the schema covers that, making the overall context complete.
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?
The schema provides 100% description coverage for both parameters, with detailed explanations for school_name and school_slug. The description adds no additional parameter-specific meaning, 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 states the tool's verb ('Get') and resource ('full admissions statistics for a specific school'), and enumerates concrete data fields (GPA/test percentiles, acceptance rate, class size, tuition, employment outcomes). This distinguishes it from sibling tools like search_schools, calculate_match_score, and compare_to_applicants, which serve different purposes.
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 specifies the tool is for a specific school and notes it is public with no authentication required. It does not explicitly mention alternatives or exclusions, but the sibling list and the phrase 'for a specific school' make the use case clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_schoolsSearch professional schoolsAInspect
Search professional schools (law, medical, dental, MBA, pharmacy, veterinary, optometry) by program type, name, or ranking range. Returns admissions stats and AdmitBase links. Public — no authentication required.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results (default 20, max 50) | |
| query | No | School name search (partial match) | |
| program | Yes | Type of professional school program | |
| max_ranking | No | Only return schools ranked below this number | |
| min_ranking | No | Only return schools ranked at or above this number |
TDQS
A4.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It discloses that the tool is public (no auth required) and that it returns admissions stats and AdmitBase links, which is useful behavioral context. However, it doesn't mention any rate limits, pagination behavior, or whether results are sorted, but for a simple search tool this is reasonably transparent.
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 with the main action front-loaded. The first sentence specifies the search criteria, the second describes the return value and public access. Every word is useful, no fluff or repetition.
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 no output schema and no annotations, the description adequately explains the purpose, parameters (via context), and return type. It doesn't explain every edge case like result sorting or exact output structure, but for a search tool that returns stats and links, it's sufficiently complete. Minor gap: no mention of default limit or possible result formats, but the schema handles limit defaults.
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 coverage is 100% with each parameter having a description. The description adds a general mapping by mentioning 'by program type, name, or ranking range' which correlates with program, query, and ranking parameters, but it doesn't provide additional semantics beyond what the schema already says. This aligns with the baseline of 3 for high schema coverage.
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 states the verb 'Search' and the resource 'professional schools' with specific filters (program type, name, ranking range), distinguishing it from sibling tools like get_school_stats by focusing on search/filter behavior and return of admissions stats and AdmitBase links.
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 implies usage context: you use this when you need to find schools by program, name, or ranking. It doesn't explicitly exclude alternatives, but the sibling tools have obviously different purposes (e.g., calculate_match_score, compare_to_applicants), making the context clear. No explicit when-not-to-use is given, 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.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
- Removed
compare_to_applicants - Removed
save_my_stats
2 tool updates
- Changed
calculate_match_score1 field changed- changed
Input schema / properties / test_score / descriptionPrevious value: -"Test score: LSAT 120-180, MCAT 472-528, DAT 1-30, OAT 200-400, GMAT/GRE for MBA, PCAT for pharmacy. Omit for ATAR-based Australian schools."New value: +"Test score: LSAT 120-180, MCAT 472-528, DAT 1-30, OAT 200-400, GMAT/GRE for MBA. Omit for pharmacy (PCAT retired 2024 — GPA-only) and for ATAR-based Australian schools."
- Changed
save_my_stats1 field changed- changed
Input schema / properties / test_score / descriptionPrevious value: -"Your test score (LSAT, MCAT, DAT, OAT, GMAT/GRE, PCAT)"New value: +"Your test score (LSAT, MCAT, DAT, OAT, GMAT/GRE; pharmacy is GPA-only since the PCAT retired)"
1 tool update
- Changed
calculate_match_score4 fields changed- added
Input schema / properties / atarAdded value: +{ + "description": "Australian Tertiary Admission Rank (0-99.95). Used instead of test_score/gpa for Australian schools, which select on a published ATAR cutoff.", + "maximum": 99.95, + "minimum": 0, + "type": "number" +} - changed
Input schema / properties / gpa / descriptionPrevious value: -"Cumulative GPA on a 4.0 scale"New value: +"Cumulative GPA on a 4.0 scale (omit for ATAR-based Australian schools)" - changed
Input schema / properties / test_score / descriptionPrevious value: -"Test score: LSAT 120-180, MCAT 472-528, DAT 1-30, OAT 200-400, GMAT/GRE for MBA, PCAT for pharmacy"New value: +"Test score: LSAT 120-180, MCAT 472-528, DAT 1-30, OAT 200-400, GMAT/GRE for MBA, PCAT for pharmacy. Omit for ATAR-based Australian schools." - changed
Input schema / requiredPrevious value: -[ - "program", - "gpa", - "test_score" -]New value: +[ + "program" +]
5 tool updates
- First observed
calculate_match_score - First observed
compare_to_applicants - First observed
get_school_stats - First observed
save_my_stats - First observed
search_schools
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TDQS
A4.3/5.0
Disambiguation4/5
Each tool has a distinct primary purpose: searching for schools, retrieving detailed stats for a known school, and calculating match probability from applicant data. There is minor overlap because search_schools also returns admissions statistics, but the difference in focus (discovery vs. details vs. personalized analysis) is clear enough.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with snake_case: calculate_match_score, get_school_stats, and search_schools. The verbs are specific and clearly indicate the action each tool performs.
Tool Count5/5
With 3 tools, the server is tightly scoped and each tool covers a necessary part of the admissions workflow: finding schools, getting statistics, and computing match scores. This is within the ideal 3-15 range and does not feel padded or sparse.
Completeness4/5
The server covers the core read-only admissions use cases: discovery, stats lookup, and personalized match calculation. A comparison feature or direct ranking list tool would be a nice addition, but agents can accomplish common tasks by combining search and stats with match scoring, so there are no critical dead ends.