Tool that tailors your existing CV/resume to a specific job posting. Beat AI screening with AI tailoring.
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Average 4.5/5 across 2 of 2 tools scored.
The two tools are clearly distinct: score_cv is a quick assessment for gap analysis, while tailor_cv performs the actual rewriting and provides deeper output. No overlap in their core actions.
Both tools follow a consistent verb_noun pattern in snake_case (score_cv, tailor_cv), making the naming predictable and uniform.
With only two tools, the count is borderline thin per the calibration scale. However, for a narrow CV-tailoring domain, the pair may be sufficient, but it feels slightly minimal.
The toolkit covers the full workflow: first score to show the gap, then tailor to improve the CV. No dead ends; the user can complete the intended task entirely.
score_cvAInspectScore how well a CV/resume matches a specific job posting. Fast and cheap — returns the original match score and the score after tailoring, plus requirement counts. Use this first to show the user the gap before a full tailor.
| Name | Required | Description | Default |
|---|---|---|---|
| cvText | Yes | The full CV/resume as plain text. | |
| jobDescription | Yes | The job posting text. |
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 discloses key behavior: returns original match score, score after tailoring, and requirement counts, plus performance traits ('Fast and cheap'). It does not explicitly state it is read-only, but the nature of scoring and the described return values imply no side effects. Given the lack of annotations, this is adequate 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 remarkably concise: two sentences, front-loaded with the primary purpose, then adding value with return details, performance characteristics, and usage directive. Every sentence serves a distinct purpose with no filler 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?
Given no output schema, the description adequately communicates the return values (original score, after-tailoring score, requirement counts). It also provides context for when to use it relative to tailoring. For a two-parameter tool with no annotations, this is complete enough for an agent to select and invoke 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?
The input schema has 100% coverage with clear descriptions for both parameters ('The full CV/resume as plain text.' and 'The job posting text.'). The description adds no extra meaning beyond the schema, which is acceptable because the schema already defines the parameters well. This meets the baseline for high schema 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's purpose with a specific verb ('Score') and resource ('CV/resume matches a specific job posting'). It also distinguishes itself from the sibling tool tailor_cv by explicitly positioning it as a pre-tailoring step: 'Use this first to show the user the gap before a full tailor.'
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 usage guidance: 'Use this first to show the user the gap before a full tailor.' This clearly indicates when to use this tool (before tailoring) and implies the alternative (tailor_cv) for subsequent tailoring. It also notes the tool is 'Fast and cheap,' helping the agent decide to invoke it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
tailor_cvAInspectTailor a CV/resume to a specific job posting: rewrites the CV with the posting's ATS keywords (never fabricating skills not already present), and returns the tailored CV, a match score, role-fit notes and interview prep. Trial is rate-limited per IP; for unlimited use the user should sign up at maxcv.ai.
| Name | Required | Description | Default |
|---|---|---|---|
| cvText | Yes | The full CV/resume as plain text. | |
| jobDescription | Yes | The job posting text. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully carries the burden of behavioral disclosure. It explicitly states a key guarantee ('never fabricating skills not already present'), lists all return elements, and reveals a rate limit for trial use plus a signup path for unlimited use. This is rich, honest behavioral context beyond the input schema.
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 compact and front-loaded: the first sentence states the purpose and core behavior, and the second provides rate-limit and signup context. Every clause contributes useful information, and no words are wasted.
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 plain-text parameters and no output schema, the description is complete: it explains what the tool does, the constraints on skills, the exact return value components, and trial limitations. An agent has enough context to select and invoke the tool appropriately.
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 descriptions cover both parameters fully (cvText and jobDescription as plain text), so schema coverage is 100%. The description adds conceptual meaning by relating the parameters to ATS keywords and existing skills, but does not add syntax, formatting, or additional parameter-specific details 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 identifies the tool's action ('Tailor a CV/resume'), the target resource ('a specific job posting'), and the core transformation (rewriting with ATS keywords). It also names specific outputs, distinguishing it from the sibling 'score_cv' tool by focusing on tailoring rather than mere 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 communicates clear use context: it is for tailoring a CV to a job posting and includes the post-tailoring outputs. It does not explicitly exclude alternatives or name 'score_cv' as an alternative, but the verb 'tailor' and the output list imply when this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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