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

With no annotations, the description carries the full burden for behavioral disclosure. It does disclose the return envelope and the case-insensitive filter behavior, which is good. However, it does not mention potential default limits, pagination, or any error conditions beyond the envelope. For a simple read tool, this is adequate but not rich.

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 purpose, and includes concise optional details. Every word serves a purpose, with no redundancy 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 tool is simple with only two optional parameters and an output schema present. The description covers the scope (listing universities), the filtering options, and the response envelope. Since the output schema exists, the description need not detail return fields. It is complete for the tool's complexity.

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 already provides 100% coverage for both parameters (limit and name_contains) with clear descriptions. The tool description merely restates these options without adding new semantic information, 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 clearly states 'List universities in the YOK ATLAS preference guide' with a specific verb and resource. It distinguishes from sibling tools like get_university_cities and get_university_programs by focusing on universities themselves, and from search_university_programs by being a list rather than a 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: it lists universities with optional filters, making it clear this is for retrieving university lists. It doesn't explicitly name alternatives, but the sibling tool names and the phrase 'in the YOK ATLAS preference guide' provide clear context. No exclusions are mentioned, but the specification is enough for an agent to infer when to use it.

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 the filtering behavior (case-insensitive substring), the optional limit, and the return envelope format ({ok, data, error, disclaimer}). It does not mention ordering, pagination, or potential edge cases, but for a simple read-only list tool, the disclosed behavior is substantial.

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, and every phrase contributes value. It is concise with no redundant or filler 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 tool is simple (2 optional parameters, no required params, output schema present). The description covers the purpose, the use case, parameter options, and the response envelope. With an output schema available, not detailing every return field is acceptable. The description is complete for the tool's complexity.

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 restates the parameter behavior ('case-insensitive substring' and 'cap the number of results') but does not add meaningful detail beyond the schema's own descriptions. It adds no new semantic information.

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 the specific verb 'List' with a clear resource: 'provinces (il) used by YOK ATLAS to filter university preference searches.' This clearly distinguishes it from sibling tools that deal with universities or programs, as it focuses on provinces only.

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: the tool is for listing provinces used in YOK ATLAS university preference searches. It implies when to use it (when needing province options for filtering) without explicitly naming alternatives, but the sibling tool names make the differentiation obvious. No exclusions or alternative guidance are given, but the use case is evident.

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 the return envelope ({ok, data, error, disclaimer}), filter behavior (case-insensitive substring, ASCII score type spellings), and the optional limit. The 'List' verb implies a read-only operation. It does not discuss error conditions or rate limits, but for a listing 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-loads the main purpose, and includes only essential details (examples, filters, envelope). Zero 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 three optional parameters, an output schema, and sibling tools. The description covers the core behavior, filter semantics, and return envelope. It is complete for a read-only listing tool, but could be enhanced by mentioning how it relates to 'search_university_programs'.

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 all three parameters. The description adds minor clarifications like 'case-insensitive substring' and 'cap the number of results', but these largely repeat what the schema says. Baseline 3 is appropriate where the schema does 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 clearly states the tool lists YOK ATLAS program groups with a concrete example ('Bilgisayar Muhendisligi'). It does not explicitly differentiate from the sibling 'search_university_programs', but the verb 'List' plus resource makes the primary purpose clear.

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 through its clear listing and filter capabilities, but provides no explicit guidance on when to use this tool versus the sibling 'search_university_programs' or other alternatives. There are no exclusions or 'when not to use' statements.

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 burden of behavioral disclosure. It discloses the return fields (university, program, quota, fee, scholarship, cutoff), pagination, and the standard envelope {ok, data, error, disclaimer}. It does not mention auth or rate limits, but as a read-oriented search tool the disclosed behavior is reasonably complete and non-contradictory.

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?

Three sentences, front-loaded with the primary purpose, then prerequisites, then return behavior. Every sentence adds essential information with no filler or repetition of schema details that aren't relevant to decision-making.

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 complexity (16 optional parameters, output schema present), the description covers the essential context: what the tool is, how it relates to siblings, what prerequisites exist, what it returns, and the response envelope. The detailed schema handles parameter specifics, so the description is appropriately 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% with each parameter already described in detail, so the baseline is 3. The description adds extra semantic value by explicitly stating that university_ids, program_group_ids, and city_codes must be resolved via sibling tools and that the tool does no name lookups—clarifying why these parameters are needed and how they relate to the rest of the system.

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 identifies the tool as the core preference-search endpoint for YOK ATLAS program placements, using a specific verb ('Search') and resource ('program placements'). It clearly distinguishes from sibling lookup tools by stating that ID resolution must happen via get_universities, get_university_programs, and get_university_cities first.

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?

Explicitly tells the agent which prerequisites must be met before calling (university_ids, program_group_ids, city_codes resolved via three named sibling tools) and notes that this tool does not do name lookups. This gives concrete when-to-use guidance and steers the agent toward alternatives when name-based search is needed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.