AI Applyd

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

Annotations already indicate readOnly, idempotent, non-destructive behavior. The description adds value by noting it is deterministic and uses no AI credits, and clarifies the requirement for a connected AI Applyd account. This enriches the behavioral 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 consists of two clear, concise sentences. The first states the core function and outputs; the second adds important contextual notes. Every sentence earns its place with no fluff.

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 (single parameter, no output schema), the description is fairly complete. It covers what the tool extracts, its deterministic nature, and the account requirement. It does not describe return format or error handling, but these are common and the tool is straightforward.

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?

There is only one parameter with 100% schema description coverage. The schema describes 'Full text of the job description' with length constraints. The tool description does not add additional parameter details beyond that, so a baseline 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 verb 'analyze' and the resource 'job description', and lists specific outputs (skills, nice-to-haves, red flags, seniority). This distinguishes it from sibling tools like 'score_resume' or 'generate_cover_letter', which have different purposes.

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 it is deterministic and uses no AI credits, giving some context for when to use, but it does not explicitly state when not to use this tool or suggest alternatives among siblings. Usage guidance is implied but not explicit.

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 honestly discloses the tool's actions (tailor resume, generate cover letter, submit) and the paid plan requirement. Annotations show destructiveHint=true which aligns with the description's submission action. 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: first clearly states purpose, second covers usage and requirement. No redundant text, front-loaded with action.

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 tool initiating automated application, the description covers inputs, process, and a requirement. It lacks details like expected output or error scenarios, but the complexity and lack of output schema make this acceptable.

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 fully documents both parameters. The description adds minimal value beyond noting that resume_text is optional and uses a default. With high coverage, 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 specifies a concrete verb-resource combination: 'Automatically apply to a job using AI.' It clearly differentiates from sibling tools that perform sub-steps (e.g., generate_cover_letter, optimize_resume) by framing the tool as the end-to-end submission process.

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 states required inputs ('Provide the job URL and optionally your resume text') and a prerequisite ('Requires paid plan'). It does not explicitly list when not to use or alternative tools, but the context of sibling tools implies those are for individual tasks.

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 readOnlyHint=false, consistent with creating a new resource. The description adds behavioral details: PDF is saved to account with a shareable link, and requires a paid plan. This goes 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?

Three sentences, no fluff. The most critical action and constraints are front-loaded: generation, templates, saving, and payment requirement.

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 2 parameters and no output schema, the description covers key aspects: input, templates, output (saved PDF with link), and payment. Minor gap: no mention of output format or link behavior, but sufficient for basic 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 coverage is 100% with clear parameter descriptions. The description rephrases template options and mentions the text input, adding context like the paid plan constraint, enhancing 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 tool generates a professional PDF resume from text, specifying template options and account saving. This distinguishes it from sibling tools like analyze_job_description or optimize_resume, which have different purposes.

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 when to use (need a PDF resume), but does not explicitly provide exclusions or alternatives. The paid plan requirement is noted, giving context on prerequisites.

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 this is a write operation (readOnlyHint=false) but not destructive. The description adds the paid plan requirement, which is valuable. However, it doesn't detail response format, privacy, or rate limits. No contradiction 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?

Two sentences with no fluff; front-loaded with purpose and required inputs. Every word 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 no output schema, the description hints at output ('personalized cover letter with key themes'). It covers inputs and constraints (paid plan). Could be more detailed about output format or error handling, but adequate for an AI agent.

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. The description redundantly mentions resume, job description, and company name but adds no new semantic detail. The tone parameter's default is only in the schema, not in the description.

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 a cover letter tailored to a job, using resume, job description, and company name. The verb 'generate' and resource 'cover letter' are specific and distinguish it from sibling tools like analyze_job_description or optimize_resume.

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 when to use (when you need a personalized cover letter) but lacks explicit when-not-to-use or alternative comparisons. The only extra constraint is the paid plan requirement, which is a limitation rather than guidance.

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 include readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds only 'Requires authentication' but does not explain the implications of openWorldHint (e.g., external API calls, variable results) or any rate limits, data handling, or side effects. With annotations already providing basic safety cues, the description contributes minimal behavioral 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 concise (two sentences) and front-loads the purpose and outputs. The structure is clear, but could benefit from breaking down the output components into a more scannable list. Nevertheless, every sentence adds value and there is no 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 generation tool with no output schema, the description enumerates the expected output types (company insights, STAR scenarios, etc.), which helps the agent understand what to expect. However, it does not mention output format, length constraints, or the impact of the optional job_description parameter. Overall, it is reasonably complete given the context.

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 clear minLength, maxLength, and descriptions for each parameter. The description does not enhance parameter guidance; it only lists output components. Baseline is 3 since the schema is sufficient, and the description adds no additional param-specific semantics.

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 as AI-powered interview preparation and lists specific outputs (company insights, STAR scenarios, likely questions, approach guidance, talking points, questions to ask, negotiation prep). This distinguishes it from sibling tools like analyze_job_description or generate_cover_letter, which focus on different tasks.

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 use for interview preparation but does not explicitly specify when to use this tool over alternatives. No guidance on prerequisites, when not to use, or how it compares to sibling tools like score_resume or search_jobs. The usage context is inferred rather than stated.

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 discloses that the tool rewrites the resume and returns optimized content and a change summary, and that authentication is required. Annotations already indicate it is not read-only (readOnlyHint: false) and not destructive (destructiveHint: false). There is no contradiction, and the description adds moderate context beyond annotations, but does not detail side effects or rate limits.

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 that front-load the core purpose, then concisely cover output details, optional parameter usage, and authentication. No superfluous text; every sentence serves a purpose.

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 tool with only two parameters and no output schema, the description covers all essential aspects: what the tool does, what to expect in the output, how to use the optional parameter, and authentication requirements. It provides sufficient context for an agent to select and invoke the tool correctly.

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 both parameters well-documented. The description adds valuable context by explaining the behavioral difference when job_description is provided versus omitted, enhancing understanding beyond the schema alone.

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?

Clearly states the tool optimizes resumes for ATS compatibility, distinguishing it from siblings like score_resume (which only scores) or translate_resume (which translates). The verb 'optimize' and resource 'resume' are specific, and the description further clarifies that it rewrites and returns content with a summary of changes.

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 explains how to use the optional job_description parameter to tailor optimization to a specific posting, and notes that omitting it results in general ATS optimization. However, it does not provide explicit guidance on when not to use this tool versus similar siblings like generate_cover_letter or auto_apply, nor does it mention prerequisites beyond authentication.

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 readOnly, idempotent, and non-destructive. The description adds value by specifying 'Deterministic keyword scoring' and 'uses no AI credits', which are behavioral traits beyond annotations. It also lists what the tool returns (score, missing keywords, section scores, recommendations), providing clear expectations. 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?

The description consists of two concise sentences with no redundant information. It front-loads the purpose and immediately follows with return values and key differentiators (deterministic, no AI credits, requirement). 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?

The tool has two simple parameters, no output schema, and moderate complexity. The description covers all key aspects: purpose, return values, key behavioral traits, and requirements. It lacks details on error handling or limits, but given the simplicity, it is largely sufficient. The absence of output schema is compensated by listing expected return items.

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. The description does not add additional semantic value beyond what the schema already provides. Since the schema carries the full burden, a 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 verb 'Score' and resource 'resume against a job description' with specific context 'for ATS compatibility'. It contrasts with the sibling 'score_resume_ai' by noting 'Deterministic keyword scoring, uses no AI credits', effectively distinguishing this tool as the non-AI alternative.

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 mentions 'uses no AI credits' and 'deterministic', implying when to choose this over AI-powered alternatives. It also states a requirement: 'requires a connected AI Applyd account'. However, it does not explicitly say 'use this tool when you want deterministic scoring' or exclude cases where AI scoring is preferred.

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 readOnlyHint and non-destructive, which description does not contradict. Description adds authentication requirement and scope of feedback. However, does not address openWorldHint implications (possible side effects beyond return 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?

Three sentences efficiently cover purpose, usage, and alternatives. Front-loaded with main functionality, no fluff.

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?

No output schema, but description lists feedback components (section-by-section, gap analysis, etc.). Covers authentication and alternative. Missing return format details but adequate for agent decision-making.

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 clear descriptions. Description adds context: job_description is optional and its omission results in general ATS readiness score, which enhances 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?

Description clearly states AI-powered ATS scoring with detailed feedback, gap analysis, requirement mapping, and keyword strategy. It distinguishes from sibling tool 'score_resume' by noting it is the free alternative for keyword-based 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?

Explicitly states when to use with job_description for specific scoring or omit for general readiness. Also provides authentication requirement and suggests alternative tool 'score_resume' for keyword-based scoring.

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 discloses that the tool updates job-match preferences, adding context beyond annotations (which show readOnlyHint=false). It does not contradict annotations. However, it could detail additional behavioral traits like rate limits or result limits.

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 concise (4 sentences) and front-loaded with the primary purpose. No redundant or extraneous text; 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 openWorldHint annotation and no output schema, the description covers the tool's purpose, returned items, side effect, and auth. It could mention pagination or result count limits for completeness, but overall it 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?

Input schema has 100% coverage with individual parameter descriptions. The description broadly mentions 'title, location, and remote preference' but does not add new meaning 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' and the resource 'job matches' with specific scope (title, location, remote preference). It distinguishes from siblings like 'analyze_job_description' and 'auto_apply' by focusing on search and listing.

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 a side effect (updates preferences) and authentication requirement, but does not explicitly state when to use this tool versus alternatives like 'analyze_job_description' or 'auto_apply'. No exclusion criteria or when-not-to-use guidance is 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?

Description adds value beyond annotations: 'saves to account' and 'requires authentication'. Aligns with readOnlyHint=false and destructiveHint=false.

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 short, front-loaded sentences with no fluff. Every sentence adds essential 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?

Covers purpose, side effect (saves copy), and auth. Lacks output format details but sufficient for simple tool without output schema.

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% so parameters are fully described. Description adds no extra meaning beyond 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?

Clearly states the action (translate), resource (resume), and target language. Distinct from siblings as no other tool translates.

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?

Purpose is clear but lacks explicit when-not-to-use or alternatives. However, the unique function makes usage obvious.

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