AdmitBase Admissions

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.

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.

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.

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.

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.

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.

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

The description adds useful behavioral context beyond the name by disclosing the Bearer auth requirement and the anonymous nature of the community reports. However, with no annotations provided, it does not explicitly state whether the operation is read-only or if any modifications occur, nor does it describe behavior on missing saved stats.

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 long and immediately states the action and a key requirement. It is front-loaded with the action ('Compare') and includes only essential information without waste.

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 covers the core purpose and authentication requirement, and the schema handles parameter documentation. However, since there is no output schema, the description would benefit from explaining what the result looks like and any prerequisites (e.g., saved stats must exist).

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 both parameters have descriptions in the schema. The tool description does not add additional parameter-level detail beyond what the schema provides, so it meets the baseline but does not exceed it.

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 action ('Compare') and the specific resource ('a user's saved stats against anonymous community outcome reports'). It distinguishes this tool from siblings by focusing on community outcome comparison, while sibling tools like calculate_match_score, get_school_stats, save_my_stats, and search_schools have different scopes.

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 clearly indicates when to use the tool: when comparing a user's saved stats against anonymous community outcomes. It also includes the auth requirement. However, it does not explicitly mention when not to use it or name alternatives, but the purpose is clear enough to infer 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, 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.

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.

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.

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.

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.

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.

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 Bearer auth requirement and where to generate a key, which is important. However, it does not mention whether the save overwrites existing records, what the response looks like, or any other side effects beyond the basic write operation.

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 main action, followed by the essential auth instruction. No unnecessary words or repetition. Every sentence 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 straightforward write operation with a well-documented schema, the description is sufficiently complete. It explains what is saved, under whose key, and the auth requirement. It lacks explicit output/error behavior, but given the simplicity and no output schema, this is not a critical gap. Overall, an agent can confidently select and invoke it.

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 has 100% parameter description coverage, so the baseline is 3. The description adds context that GPA and test score are the primary values being saved per program, but it does not add format or syntax details beyond what the schema already provides. It does not fully compensate for any gaps because there are none.

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 specific action: 'Save a user's GPA and test score per program under their AdmitBase API key.' This distinguishes it from the sibling read/analysis tools (calculate_match_score, compare_to_applicants, get_school_stats, search_schools) which are all query-oriented.

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: it is the save/write operation for application stats, with an explicit auth prerequisite. However, it does not explicitly compare to alternatives or state when not to use it. The purpose is strong enough that an agent can infer when to use it, but explicit exclusions are missing.

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 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.

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.

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.

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.

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.

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.