SiteCheck Jobs & Property

Resume vs job-ad keyword check, the way applicant tracking

jobs_cv_optimizer

Read-only

Resume vs job-ad keyword check, the way applicant tracking systems screen: extracts the job ad's key terms (words and two-word phrases, weighted by frequency), shows which the resume already covers and which are missing, and gives a 0-100 coverage score and the top terms to add. Plain text analysis of the two texts you send; no AI rewriting and no data is stored. Related tools on this server, for a different need: jobs_cv_library_uk_jobs when you need CV-Library jobs for a location and optional keyword: job. Price: $0.002 in USDC per successful call, paid with x402 (Base, Solana or Arc); failed calls are not charged.

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": {
      "resume": "<resume>",
      "job": "<job>"
    },
    "name": "jobs_cv_optimizer"
  }
}

Input Schema

Name	Required	Description
`job`	Yes	Job ad plain text, up to 4,000 characters (required)
`top`	No	How many job terms to score, 10 to 60 (default 30)
`resume`	Yes	Resume / CV plain text, up to 6,000 characters (required; sent as a query parameter)

Output Schema

Name	Required	Description	Default
`note`	No
`score`	No
`covered`	No
`missing`	No
`suggestions`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 23, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Beyond readOnlyHint/openWorldHint the description discloses that analysis is plain-text only, that no data is stored, that there is no AI rewriting, and that billing is $0.002 USDC per successful call via x402 with failed calls uncharged. The payment/auth model and data-handling behavior are exactly the kind of context annotations cannot convey.

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

Conciseness4/5

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

The core mechanism and output are front-loaded in one dense sentence, and the pricing note earns its place. The mid-sentence pointer to jobs_cv_library_uk_jobs is the weakest part — it is a different-need tool in a long sibling list and adds bulk without much decision value.

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

Completeness5/5

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

An output schema exists, yet the description still previews the return (coverage score plus top terms to add), and it covers cost, data handling, and input nature for a simple three-parameter read-only tool. Nothing an agent needs before calling is missing.

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 description coverage is 100%, so the character limits, default, and range for `top` are already documented structurally. The description adds conceptual meaning by explaining that terms are words and two-word phrases weighted by frequency and that `top` governs how many terms are scored, but gives no extra syntax or edge-case guidance.

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?

The description states a specific verb and resource: it compares a resume against a job ad, extracts weighted key terms, reports covered vs missing terms, and returns a 0-100 coverage score. That is far more specific than the tool name alone and distinguishes it from every jobs_* listing/scraper sibling.

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?

It establishes the use case (applicant-tracking-style screening) and rules out a common wrong expectation with 'no AI rewriting', plus points to jobs_cv_library_uk_jobs for a different need. It stops short of explicit when-not conditions or a true same-task alternative, so it is clear context rather than full routing.

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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