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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what information is returned (comprehensive details including multiple categories) which is helpful, but doesn't mention important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or error conditions. The description adds value but doesn't fully compensate for the lack of annotations.

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 well-structured and front-loaded with the core purpose, followed by parameter documentation and return value details. Every sentence earns its place by providing essential information without redundancy. The bulleted list of return categories is efficient and informative.

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 that an output schema exists (though not shown here), the description doesn't need to fully explain return values. It provides a comprehensive overview of what information is returned through the bulleted list. However, for a tool with no annotations, it could benefit from more behavioral context about how the tool operates, potential limitations, or error handling.

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 both parameters thoroughly. The description adds marginal value by providing examples ('e.g., 2024, 2023') and clarifying that yop_kodu is a 'unique identifier for the associate degree program' - though similar information exists in the schema. The description also provides context about what the parameters enable (getting data for a specific program in a specific year).

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 ('Get comprehensive details'), resource ('associate degree program from YOKATLAS Atlas'), and scope ('specific associate degree program'). It distinguishes from sibling tools by specifying associate degree programs (vs. bachelor degree tools) and details retrieval (vs. search tools).

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 about when to use this tool (to get comprehensive details for a specific associate degree program when you have the YÖP code and year). However, it doesn't explicitly state when NOT to use it or mention alternatives like the search_associate_degree_programs tool for when you don't have the specific code.

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 indicates this is a read operation ('Get') and lists the types of information returned, but doesn't disclose behavioral traits like rate limits, authentication needs, error conditions, or whether the data is real-time vs. cached. It adds some context about what data is included but lacks operational details.

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 well-structured and front-loaded with the core purpose. It uses bullet points efficiently to detail return information without redundancy. Every sentence earns its place by clarifying scope and outputs.

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 moderate complexity (2 parameters, read-only), 100% schema coverage, and the presence of an output schema (implied by 'Returns detailed information'), the description is complete enough. It specifies the resource scope, parameters, and return data types, which aligns well with the structured data provided.

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 both parameters thoroughly. The description adds minimal value by listing parameters and providing examples, but doesn't explain semantics beyond what's in the schema (e.g., what YÖP code format entails). Baseline is 3, but it gets a point for reinforcing parameter importance with the examples.

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 comprehensive details' and specifies the resource as 'a specific bachelor's degree program from YOKATLAS Atlas.' It distinguishes from sibling tools by focusing on bachelor's degree programs (vs. associate degree tools) and on retrieving details for a specific program (vs. search tools).

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 by specifying it's for retrieving details of a specific bachelor's degree program, which differentiates it from search tools. However, it doesn't explicitly state when to use this tool versus alternatives like 'search_bachelor_degree_programs' or provide exclusions (e.g., not for associate degrees).

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 of behavioral disclosure. It describes smart features like fuzzy matching, partial matching, normalization, and validation, which are useful behavioral traits. However, it doesn't mention performance characteristics (e.g., rate limits), error handling, or what the output looks like (though an output schema exists). It adequately covers search behavior but lacks broader operational context.

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 well-structured with sections for smart features, parameters, and a note, making it easy to scan. It's appropriately sized for a complex tool with 19 parameters. However, some redundancy exists (e.g., parameter explanations partially repeat schema descriptions), and the note about TYT scores could be more integrated.

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 (19 parameters, low schema coverage, no annotations) and the presence of an output schema, the description is reasonably complete. It explains the tool's purpose, key parameters, and smart features, and the output schema handles return values. However, it doesn't fully address all parameters or provide usage examples, leaving some gaps for such a parameter-rich tool.

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 42% (low), but the description compensates by listing and briefly explaining 7 key parameters (university, program, city, university_type, fee_type, education_type, results_limit) with examples. It adds meaning beyond the schema by grouping them and noting smart features like fuzzy/partial matching. However, it doesn't cover all 19 parameters, leaving some undocumented (e.g., yop_kodu, ust_puan).

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: 'Search for associate degree (önlisans) programs with smart fuzzy matching and user-friendly parameters.' It specifies the verb ('search'), resource ('associate degree programs'), and distinguishes from siblings by focusing on associate degrees versus bachelor degrees in sibling tools like 'search_bachelor_degree_programs'.

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 to use this tool: for searching associate degree programs, with a note that they use TYT scores, not SAY/EA/SOZ/DIL like bachelor programs. This implicitly distinguishes it from 'search_bachelor_degree_programs', but it doesn't explicitly state when not to use it or mention alternatives like 'get_associate_degree_atlas_details' for detailed information.

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 full burden. It describes smart matching features (fuzzy, partial) and intelligent parameter normalization, which are valuable behavioral insights. However, it doesn't disclose important aspects like rate limits, authentication requirements, error handling, or whether this is a read-only operation (though 'search' implies it likely is).

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 well-structured with clear sections (overview, smart features, parameters). It's appropriately sized for a complex tool with many parameters. Some redundancy exists between the parameter explanations and schema descriptions, but overall it's efficient and front-loaded with the most important information.

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 complex tool with 17 parameters, 53% schema coverage, and no annotations, the description does a reasonable job explaining the core functionality and primary parameters. However, it doesn't address the many additional parameters in the schema (uni_adi, program_adi, etc.), and while an output schema exists, the description provides no context about result format or structure.

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?

With schema description coverage at only 53%, the description compensates well by listing and briefly explaining all 8 primary parameters with practical examples. It clarifies matching behavior for university, program, and city parameters, and explains the purpose of results_limit. However, it doesn't cover the 9 additional parameters (uni_adi, program_adi, etc.) that appear in 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 searches for bachelor's degree programs with specific smart features. It distinguishes from sibling tools by focusing on bachelor's degrees (vs. associate degrees in siblings), but doesn't explicitly contrast with 'get_bachelor_degree_atlas_details' which appears to be a detail retrieval tool rather than a search tool.

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 no guidance on when to use this tool versus alternatives like 'get_bachelor_degree_atlas_details' or 'search_associate_degree_programs'. It mentions smart features but doesn't explain when these features are beneficial or when other tools might be more appropriate.

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