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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions comparing specifications across criteria and includes evolution analysis, but it doesn't disclose key behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, response time, or what happens if invalid IDs are provided. For a tool with 4 parameters and no annotations, this is a significant gap in transparency.

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, efficient sentence that front-loads the core purpose ('Compare multiple 3GPP specifications') and lists key criteria. It avoids redundancy and waste, making it appropriately sized for the tool's complexity. However, it could be slightly more structured by separating usage hints or behavioral details, but it's concise and clear.

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 (4 parameters, no annotations, no output schema), the description is incomplete. It covers the basic purpose but lacks behavioral context (e.g., safety, performance), usage guidelines, and details on output format or error handling. Without annotations or an output schema, the description should do more to compensate, but it falls short, leaving gaps for an AI agent to operate effectively.

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 description adds minimal meaning beyond the input schema, which has 100% coverage. It lists example criteria (architecture, procedures, evolution, implementation differences), which loosely maps to the 'comparison_criteria' parameter but doesn't provide additional syntax, constraints, or context beyond what's in the schema descriptions. With high schema coverage, the baseline is 3, and the description doesn't significantly compensate or add 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 the tool's purpose: comparing 3GPP specifications across specific criteria like architecture, procedures, evolution, and implementation differences. It uses the verb 'compare' with the resource '3GPP specifications' and lists example criteria, making the function specific. However, it doesn't explicitly distinguish this tool from sibling tools like 'search_specifications' or 'get_specification_details', which might also involve specification analysis, so it misses full differentiation.

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. It lists criteria for comparison but doesn't mention sibling tools (e.g., 'find_implementation_requirements' or 'get_specification_details') or specify contexts where this comparison is preferred over other operations. There's an implied usage based on the criteria, but no explicit when/when-not instructions or alternatives are provided.

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 of behavioral disclosure. It mentions what the tool extracts (requirements, dependencies, guidance) but lacks critical behavioral details: it doesn't specify if this is a read-only operation, what data sources it queries, potential rate limits, authentication needs, or error handling. For a tool with 6 parameters and no annotations, this is a significant gap in transparency.

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, efficient sentence that front-loads the core purpose ('Extract detailed implementation requirements...') and lists key components. There's no wasted text, but it could be slightly more structured by explicitly separating the tool's function from its outputs for better clarity.

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 (6 parameters, no output schema, no annotations), the description is moderately complete. It covers the what (extract requirements) and scope (3GPP features) but lacks context on how it operates, data sources, or output format. Without annotations or an output schema, more behavioral and usage details would improve completeness for effective agent use.

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%, meaning all parameters are well-documented in the input schema itself. The description adds minimal value beyond the schema by implying the tool analyzes features for implementation details, but it doesn't provide additional context on parameter interactions or usage examples. With high schema coverage, the 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 purpose: 'Extract detailed implementation requirements for specific 3GPP features' with specific components listed (mandatory/optional requirements, dependencies, implementation guidance). It uses the verb 'extract' with the resource 'implementation requirements' and scope '3GPP features.' However, it doesn't explicitly differentiate from sibling tools like 'compare_specifications' or 'get_specification_details,' which might also involve 3GPP specifications.

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. It doesn't mention sibling tools or specify contexts where this tool is preferred, such as for implementation planning versus specification comparison. There's no indication of prerequisites or exclusions, leaving usage decisions unclear.

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 for behavioral disclosure. While it mentions what information is included (metadata, content, dependencies), it doesn't describe response format details, potential rate limits, authentication requirements, error conditions, or whether this is a read-only operation. The description provides basic functional information but lacks important behavioral context for a tool with 4 parameters.

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, well-structured sentence that efficiently communicates the tool's purpose and scope. It's appropriately sized for the tool's complexity, front-loading the core functionality. While concise, it could potentially benefit from slightly more detail given the lack of annotations and output schema.

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 4 parameters, no annotations, and no output schema, the description provides adequate basic information but lacks completeness. It explains what the tool does but doesn't address important contextual elements like response format expectations, error handling, or how the different parameters interact to affect the returned data. For a tool with this complexity level, more contextual information would be helpful.

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 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema - it doesn't explain how parameters interact, provide usage examples, or clarify edge cases. Baseline 3 is appropriate when the schema does the heavy lifting, though the description could have added value by explaining parameter relationships.

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') and resource ('specific 3GPP specification'), and lists the types of details included (metadata, content, dependencies, related information). It distinguishes from siblings by focusing on comprehensive details for a single specification rather than comparison, requirements, or search functions. However, it doesn't explicitly contrast with sibling tools in the description text itself.

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 when comprehensive details about a specific 3GPP specification are needed, but doesn't explicitly state when to use this tool versus alternatives like 'search_specifications' for broader searches or 'compare_specifications' for comparisons. No explicit exclusions or prerequisites are mentioned, leaving usage context somewhat implied rather than clearly defined.

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 discloses the data sources (TSpec-LLM dataset and official metadata) and mentions the return format is 'optimized for AI agents,' which adds useful context. However, it doesn't address rate limits, authentication needs, error conditions, or pagination behavior that would be important for a search 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?

The description is perfectly concise with two sentences that each earn their place. The first sentence establishes purpose and scope, while the second clarifies return values and agent optimization. There's zero wasted language or redundancy.

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 search tool with 6 parameters, 100% schema coverage, and no output schema, the description is adequate but has gaps. It covers purpose and return types but lacks behavioral details like rate limits, error handling, or result structure. Without annotations or output schema, the agent must infer these from the tool name and parameters alone.

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 fully documents all 6 parameters. The description adds no additional parameter information beyond what's in the schema. The baseline score of 3 is appropriate when the schema does all the parameter documentation work.

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 ('Search'), resource ('3GPP specifications'), data sources ('TSpec-LLM dataset and official metadata'), and return values ('actual specification content and structured metadata'). It distinguishes itself from siblings by focusing on search functionality rather than comparison, requirements finding, or detailed retrieval.

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 this tool should be used for searching specifications, but provides no explicit guidance on when to choose this tool versus its siblings (compare_specifications, find_implementation_requirements, get_specification_details). The context is clear but lacks specific alternatives or exclusions.

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