Tool that tailors your existing CV/resume to a specific job posting. Beat AI screening with AI tailoring.
Scored across 2 tools
The two tools have clearly distinct actions: score_cv evaluates match quality, while tailor_cv rewrites the CV. The description even instructs to use score_cv first, reinforcing the separation.
Both names follow a clean snake_case verb_noun pattern (score_cv, tailor_cv) with the same object, making the set predictable and readable.
Two tools is on the thin side for an MCP server, even for a focused CV-tailoring service. The pair covers the core workflow, but the surface feels minimal.
The score-and-tailor pair covers the main user journey without obvious dead ends. Minor gaps exist—no explicit CV parsing, export, or retrieval operations—but they are not required for the stated purpose.
Score 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 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 network; 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, the description carries the full burden and does well: it discloses the no-fabrication constraint, enumerates what is returned (tailored CV, match score, role-fit notes, interview prep), and warns of per-network trial rate limits plus the sign-up path for unlimited use. Missing only auth/permission or retry/error behavior.
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 core action and outcome list, then the operational caveat. No filler or repetition.
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 exists, so the description usefully enumerates the return contents, and the constraint, rate-limit, and sign-up notes round out what an agent needs to call and explain the tool. Nothing material is missing.
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% and both parameters are self-explanatory, so the schema already does the work. The description references the CV and job posting only implicitly, adding no format or length guidance 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?
States a specific verb+resource and scope: 'Tailor a CV/resume to a specific job posting' followed by the concrete transformation (ATS keyword rewriting). The output list (tailored CV, match score, role-fit notes, interview prep) makes it clearly distinct from a pure scoring tool.
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 the use case (tailoring to a job posting) and adds rate-limit/sign-up guidance, which is useful operational context. However, it never explicitly states when to prefer this over the sibling score_cv, so the main routing decision is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
score_cvtailor_cv
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