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

The description includes a comprehensive 'Behavioral Transparency' section covering side effects (read-only, stateless), authentication requirements, rate limits, error handling, idempotency, and data privacy. Since no annotations are provided, the description fully carries the burden of disclosure and does so thoroughly.

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 (Behavior, When to use, When NOT to use, Args, Behavioral Transparency). It front-loads the purpose. However, there is some redundancy (e.g., read-only stated in both 'Behavior' and 'Behavioral Transparency'). Could be slightly more concise.

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 (2 parameters, output schema exists), the description covers behavior, usage guidance, and transparency thoroughly. It does not detail the output format (but output schema covers that). It is complete for an analyst tool with good annotations.

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 0%, so the description must compensate. However, the 'Args' section only restates parameter names and types with generic descriptions (e.g., 'api_key (str): The api key to analyze or process'). The api_key description is misleading and does not clarify its role (authentication). The description adds minimal semantic value beyond 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's purpose: 'Analyze a job description text and extract structured requirements.' This is a specific verb-resource combination. It is distinct from sibling tools (check_bias, generate_job_description, suggest_salary_range), which focus on bias checking, generation, and salary estimation, respectively.

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 includes explicit 'When to use' and 'When NOT to use' sections. It provides general guidance for structured analysis but does not directly reference sibling tools or provide comparative scenarios. The guidance is clear but lacks differentiation from alternatives.

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 thoroughly covers side effects (read-only, stateless), authentication (none for basic, pro requires key), rate limits (10/day free, unlimited pro), error handling (structured errors), idempotency (fully idempotent), and data privacy (local processing). This is comprehensive and exceeds typical 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?

Well-structured with sections, but redundant: the initial 'Behavior' paragraph repeats information from the later 'Behavioral Transparency' section. Could be more concise by removing duplication.

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 existence of an output schema (not shown), description doesn't need to explain return values. It covers rate limits, error handling, idempotency, and data privacy. However, it lacks details on the output structure (e.g., list of biased terms with alternatives) and could clarify the api_key parameter's optionality.

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 0%, so description must compensate. It provides basic descriptions for 'text' and 'api_key', but the api_key description ('The api key to analyze or process') is unhelpful and does not clarify its optional nature or purpose. Only partial value added.

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 states a specific verb ('Check'), resource ('job description'), and action ('suggest alternatives'). It clearly distinguishes from siblings like 'analyze_requirements' or 'generate_job_description' by focusing on bias and inclusivity.

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?

Includes explicit 'When to use' and 'When NOT to use' sections, providing context and exclusion criteria. However, the 'when to use' is somewhat generic ('structured analysis or classification') and does not differentiate from sibling tools.

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 shoulders the full burden and delivers comprehensively. It explicitly states read-only, no side effects, deterministic output, authentication details, rate limits (free/pro tiers), error handling, idempotency, and data privacy. Every behavioral trait is disclosed clearly and accurately.

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-organized with clear sections, but it contains redundancy—the 'Behavior' paragraph largely overlaps with the 'Behavioral Transparency' section. The 'Args' list is verbose without adding value. A more streamlined version would achieve the same clarity in fewer 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 behavioral information is complete, but the parameter descriptions are absent. Given the tool has 6 parameters with 0% schema coverage, the description should elaborate on each parameter's role in generating a job description. The existence of an output schema is noted but doesn't compensate for missing input semantics. Overall, the description feels incomplete for a tool with moderate 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 'Args' block lists parameters but adds zero meaning beyond the schema—each parameter is described generically as 'The <name> to analyze or process.' The schema itself lacks descriptions (0% coverage), so the description should compensate, but it fails to explain what each parameter means for job description generation (e.g., how 'remote' affects the output, what 'skills' format is expected). This is a critical gap.

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 'Generate a complete job description for a given role and level.' The first sentence is specific and aligned with the tool name. However, the later 'When to use' section uses generic language ('structured analysis or classification') that could apply to sibling tools like analyze_requirements, slightly diluting clarity.

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?

Explicit 'When to use' and 'When NOT to use' sections are present. The description advises against real-time production decision-making without human review, which is helpful. However, it does not directly contrast with siblings (analyze_requirements, check_bias, suggest_salary_range) to guide selection, so it loses a point.

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 and delivers a comprehensive 'Behavioral Transparency' section covering side effects, authentication, rate limits, error handling, idempotency, and data privacy.

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, but contains some repetition (e.g., behavior section overlaps with behavioral transparency). Slightly verbose but organized.

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, has output schema), the description covers all necessary aspects: purpose, usage guidelines, behavioral transparency, and parameter meaning.

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?

Although schema description coverage is 0%, the description includes an 'Args' section with brief explanations for each parameter, adding context beyond the schema. The main description also clarifies role, level, region, and api_key usage.

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 it suggests a competitive salary range based on role, level, and region. The verb 'suggest' and resource 'salary range' are specific, and among sibling tools, it uniquely handles salary analysis.

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?

Explicit 'When to use' and 'When NOT to use' sections provide clear context, including that it is for structured analysis and not for real-time production decisions without human review.

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