AI Applyd

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

Annotations already declare readOnlyHint=true, and the description confirms non-destructive keyword extraction. It adds behavioral context about using AI credits, which is beyond annotations. No contradictions.

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 with no wasted words. The first sentence front-loads the purpose, and the second provides essential prerequisites. Excellent conciseness.

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 simple, read-only tool with one parameter and no output schema, the description adequately explains the input and outcome. It mentions what will be extracted (keywords and requirements), though it could detail the output format or error conditions.

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% description coverage for the single parameter. The tool description does not add meaning beyond what the schema already provides. 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 it extracts ATS keywords and requirements from a job posting, which is a specific verb-resource pair. It differentiates from sibling tools like 'optimize_resume' and 'score_resume' which operate on resumes rather than job descriptions.

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 mentions prerequisites (connected account, AI credits) but does not provide guidance on when to use this tool versus alternatives like 'search_jobs' or 'optimize_resume'. Context for use is implied but not explicitly compared to siblings.

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 confirms the destructive action (tailoring resume/cover letter, moving through pipeline), aligning with the 'destructiveHint' annotation. It adds value by specifying the plan requirement and pipeline movement, going beyond the annotation's binary flag.

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 exceptionally concise: two sentences, front-loaded with the core purpose, and no extraneous information. 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?

Given the tool's simplicity (one parameter, no output schema) and the presence of annotations, the description sufficiently covers purpose, constraints, and process. No gaps identified.

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 single parameter 'job_url' is fully described in the input schema; the description adds no further semantics. With 100% schema coverage, the baseline score of 3 applies, as the description does not enhance parameter understanding.

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 'apply' and the resource 'specific job posting'. It distinguishes from sibling tools by emphasizing it applies to one job and moves it through the pipeline, unlike analysis or generation 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 notes the tool requires a paid plan and follows existing review settings, providing context on when it can be used. However, it does not explicitly contrast with alternative tools like 'optimize_resume' or 'generate_cover_letter', leaving some ambiguity.

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 value beyond annotations by detailing that the resume is private by default, requires a paid plan, and that make_public exposes contact details. No contradictions with 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?

Three sentences cover all key points without redundancy. The structure is logical, though slightly dense; could benefit from bullet points for readability.

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 simple tool (3 params, no output schema), the description covers purpose, inputs, templates, privacy, and pricing. It hints at the output (editable resume with PDF download), which is sufficient.

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 100% schema coverage, the description enriches the make_public parameter with a clear warning and usage guideline, and confirms template options. The schema already covers basic info, so this is solid added context.

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 creates a formatted resume from raw text and outputs it as an editable PDF, distinguishing it from sibling tools that analyze, optimize, or score resumes.

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?

It specifies when to use (to build a PDF from raw text), template choices, default privacy, and when to set make_public. However, it does not explicitly state when not to use (e.g., if user already has a formatted resume).

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?

Annotations show readOnlyHint=false and openWorldHint=true. The description adds that the tool saves the result to the account, explaining the side effect. It does not detail overwriting behavior or retrieval methods, but adds useful context beyond 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 very concise: three sentences that front-load the core action, followed by outcome and requirement. No redundant information; every sentence adds value.

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 main action, saving behavior, and plan requirement. It could be more complete by specifying the return format (e.g., plain text) and handling of existing cover letters, but given no output schema, it adequately informs usage.

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 descriptions for both parameters. The tool description does not add new information about parameter values or syntax beyond what the schema provides, so baseline 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 writes a cover letter for a specific job using the saved resume. It distinguishes from sibling tools like aiapplyd_optimize_resume (modifies resume) and aiapplyd_generate_interview_questions by focusing on cover letter generation.

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 indicates when to use the tool (to generate a cover letter based on a saved resume) and mentions the paid plan requirement. However, it does not explicitly exclude use cases or compare with alternatives like aiapplyd_auto_apply.

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 notes 'Requires authentication' but otherwise adds little beyond the annotations, which already declare readOnlyHint=true and openWorldHint=true. The description could disclose output format or API behavior, but it does not contradict annotations. Given the annotation coverage, a 3 is appropriate.

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 with no superfluous words. The first sentence front-loads the core purpose ('AI-powered interview preparation'), and the second lists outputs efficiently. Ideal conciseness.

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 enumerates the types of outputs (company insights, STAR scenarios, etc.) but lacks details on format, size, or any limitations. Since there is no output schema, the description should provide more structural expectations, but it is still fairly complete for a generation 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 coverage is 100% with each parameter described in detail (e.g., job_title maxLength, job_description optional but recommended). The description does not add any parameter-level semantics beyond the schema, so baseline 3 applies.

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 generates interview preparation content including company insights, STAR scenarios, likely questions, and negotiation prep. It distinguishes itself from siblings like aiapplyd_generate_cover_letter or aiapplyd_analyze_job_description by specifying the unique outputs, though not explicitly contrasting them.

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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, context, or scenarios where this tool is appropriate vs. others like aiapplyd_generate_cover_letter or aiapplyd_score_resume.

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?

Annotations indicate mutability (readOnlyHint=false) and side effects (openWorldHint=true), which are consistent with the description's statement that it 'rewards your resume'. The description adds context on requiring authentication and returning a summary, but does not disclose potential consequences like overwriting the original file or rate limits. Given annotation coverage, the bar is lower, and the description adds value.

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 three sentences, each conveying essential information: the core action, the return value, and the optional parameter's use. It is front-loaded and free of extraneous 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?

With 2 parameters, no output schema, and adequate annotations, the description covers all necessary aspects: tool purpose, parameter guidance, authentication, and return value format. No significant gaps remain.

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 input schema fully describes both parameters (100% coverage). The description adds meaning by explaining the optional nature of job_description and its effect on tailoring vs. general optimization, going beyond the schema's basic descriptions.

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 rewrites resumes for ATS compatibility, returning optimized content and a summary of changes. This distinguishes it from siblings like aiapplyd_score_resume (scoring) and aiapplyd_generate_cover_letter (different output), making the purpose unambiguous.

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 advises providing a job_description to tailor to a specific posting or omitting it for general optimization. It also mentions authentication requirement. While it provides clear context on when to use the optional parameter, it does not explicitly compare to alternatives like aiapplyd_score_resume, leaving room for improvement.

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 context beyond annotations by specifying that the tool returns concrete improvement suggestions and uses AI credits. Annotations already indicate readOnlyHint=true and destructiveHint=false, which align with the read-only scoring nature. No contradictions; the description enhances 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 three concise sentences, front-loaded with the primary action and outcome. Every sentence provides essential information without 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?

Despite lacking an output schema, the description comprehensively lists all return values (score, section scores, keywords, suggestions) and prerequisites (account, credits). It sufficiently covers what an agent needs to understand the tool's usage and output.

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 100% schema coverage, the description adds significant value by explaining the optional nature of job_description and its behavior when omitted. This clarifies parameter semantics beyond the schema descriptions.

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 'Score a resume against a job description for ATS compatibility' and lists specific outputs (overall score, section scores, keywords, suggestions). This distinguishes it from siblings like 'aiapplyd_optimize_resume' which focuses on optimization rather than scoring.

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 explicitly explains when to provide a job_description and when to omit it for a general ATS score. It also notes the requirement for an AI Applyd account and use of credits. However, it does not explicitly state when not to use this tool versus siblings, which would have earned a 5.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description reinforces these by stating it is read-only and does not change roles. It adds the behavioral constraint of requiring authentication, which is not covered by annotations. The description provides sufficient behavioral context beyond the 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 extremely concise, consisting of two sentences. The first sentence front-loads the core purpose and output, and the second sentence provides a crucial behavioral note and alternative. No superfluous 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?

The tool has 3 parameters (1 required) and no output schema. The description explains the return value (matched jobs with match scores, companies, application URLs) and covers authentication requirements and side effects. With high-quality annotations, this is complete for a search 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 100%, with each parameter having a clear description (e.g., job_title, location, remote_only). The description lists these parameters in the first sentence but does not add significant new semantics or examples beyond what the schema already provides. Baseline 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 verb 'Search', the resource 'AI-curated job matches', and the filtering criteria (title, location, remote preference). It also distinguishes itself from the sibling tool aiapplyd_update_job_preferences by explicitly stating what it does not do.

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 explicit context: it is read-only and does not change which roles the system hunts for. It directs the user to the sibling tool for modifying preferences. It also states the requirement for authentication, giving clear guidance on when and how to use the tool.

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?

Beyond annotations, the description clarifies that the original resume is untouched and a new resume is saved, adding non-obvious behavior. It also states authentication is needed, which is not in 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?

Two sentences, front-loaded with the verb and resource, no filler. Every sentence adds value.

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 simple tool with one parameter and no output schema, the description is complete: it explains the action, effect, and a prerequisite, requiring no further detail.

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 covers the parameter with a clear description and examples, so the tool description adds no additional meaning, meeting the baseline for 100% 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 tool translates a resume and saves a new one without altering the original, which is specific and distinct from sibling tools like optimize or score.

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 mentions requiring authentication but does not provide explicit guidance on when to use this tool versus alternatives, leaving it implied by the tool name and purpose.

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?

Beyond the annotations (destructiveHint=true), the description explains that this REPLACES the saved lists, requiring the full list to be passed. It also mentions 'Requires authentication', adding context not in 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?

Two sentences, front-loaded with the main purpose, no extraneous words. Every sentence adds value.

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 annotations and full schema coverage, the description is sufficient. It covers replacement semantics, authentication, and usage condition. No output schema needed for a typical update operation.

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?

While schema coverage is 100% and each parameter has a description, the tool description adds crucial guidance: 'pass the full list you want, not just an addition', clarifying the replacement behavior for the array parameters.

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 'Change' and resource 'which roles and locations AI Applyd hunts for on your behalf', distinguishing it from siblings like search_jobs (which searches based on these preferences) and analyze_job_description (which analyzes a specific job).

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 explicitly says 'Only call this when the user explicitly asks to change what they are looking for', providing clear guidance on when to use. It also implies not to call it for just searching or analyzing jobs.

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