maxcv — CV tailoring

score_cv

Assess the supplied CV only (assessmentKind=source_only), without generating a document or predicted after score. With v2b, method=offer-phrase-coverage-v1 measures exact positive coverage of frozen phrases copied from offer requirements and duties, with phrase counts and matched/missing lists; an empty inventory scores zero. Other engines retain method=jev-probability-v1 with semantic per-requirement probabilities. Neither is an employer ATS score or hiring probability. Use tailor_cv for actual before/after documents.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "cvText": "<cvText>",
      "jobDescription": "<jobDescription>"
    },
    "name": "score_cv"
  }
}

Input Schema

Name	Required	Description	Default
`cvText`	Yes	The full CV/resume as plain text.
`jobDescription`	Yes	The job posting text.

Schema Changelog

Changes observed during successful MCP inspections.

First observedJun 16, 2026

TDQS

A4/5.0

Behavior4/5

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 that no document is produced, explains the two scoring methods and what each measures, notes the empty-inventory-scores-zero edge case, and explicitly denies that the output is an ATS score or hiring probability. It does not describe the response shape in full or warn about input size limits, so it is strong but not exhaustive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core purpose and the sibling routing are front-loaded, which is good, but the middle is heavy with version-specific jargon (v2b, method=offer-phrase-coverage-v1, jev-probability-v1) that is dense and slow to parse. The content is relevant but not tightly trimmed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-required-string tool with no annotations and no output schema, the description covers what the score means, how it is computed, what it returns at a high level (phrase counts, matched/missing lists), and what it is not. The main remaining gap is a fuller account of the return structure, which the absent output schema leaves to the description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so cvText and jobDescription are already documented; the description only implicitly references them ('supplied CV', 'phrases copied from offer requirements and duties'). It adds no format, length, or validation guidance beyond what the schema provides, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Assess the supplied CV only') and immediately bounds it with what it is not ('without generating a document or predicted after score'), then names the sibling tailor_cv as the contrasting tool. An agent can distinguish this from tailor_cv without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly routes document-generating use cases to tailor_cv ('Use tailor_cv for actual before/after documents'), giving a clear alternative and the condition that selects it. It does not spell out other when-not conditions (e.g., when a job description is absent or CV is non-English), so it falls short of a full 5.

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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