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Check your own resume against one job post: what it asks for, what your resume shows or misses, format problems (signed)

resume_job_match

Read-onlyIdempotent

Check your own resume against one job post. Send resume_text (your resume as plain text), job_post (the post as plain text) and own_use true (it is your own resume). Answer: the skills, tools, credentials, years of experience and degree the post asks for, marked required, preferred or mentioned, each covered or missing in your resume (and in which resume section it shows); words the post repeats that your resume never uses; resume format problems (sections, contact details, dates, length, layout, keyword stuffing); a signed, dated receipt. Rules and a word list we wrote; no AI model; free, no sign-up, 30 checks a day per caller. The text of your resume and the post is never stored: it is dropped after the call (only short-lived call counters and salted hashes sit in memory for a day, to enforce the limits), and the answer never repeats your resume's words. Email addresses, phone numbers and web links are cut out of the resume before reading. English only. For the seeker's own resume only: never to rank or reject candidates for an employer. A missing word is not a missing skill, and a covered word is not proof; add a word only if it is true for you. Each answer offers the next job through the Hub (https://llpro.ai/hub) and the Job-Search Agent (https://llpro.ai/jobs).

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_text": "<resume_text>",
      "job_post": "<job_post>",
      "own_use": "<own_use>"
    },
    "name": "resume_job_match"
  }
}

Input Schema

Name	Required	Description
`own_use`	Yes	Must be true: this is the caller's own resume, checked for the caller's own job search, not a candidate's resume checked for an employer.
`job_post`	Yes	One job post as plain text, 120 to 12,000 characters. Keep its headings (Requirements, Preferred...) and bullets.
`resume_text`	Yes	Your own resume as plain text, 200 to 12,000 characters. Keep section headings (Experience, Education, Skills) and line breaks.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 13, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Annotations only say readOnly/non-destructive/idempotent/closed-world; the description adds the substance behind them: rules-based with no AI model, free, 30 checks/day per caller, text dropped after the call, only salted hashes and counters kept in memory for a day, and PII (emails, phones, links) stripped before reading. It also discloses a caveat about interpretation (a missing word is not a missing skill) and that the answer never echoes resume wording.

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 purpose is front-loaded, but the body is a long semicolon-chained run-on that mixes outputs, privacy, rate limits, caveats and promotional hub/agent links in one block. Most clauses carry real information, yet the promotional pointers and some restatement could be trimmed, and the lack of paragraphing hurts scannability.

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?

With no output schema, the description still tells the agent what the answer contains and what it will not contain (no repeat of resume words), plus privacy, limits, language and eligibility constraints. Nothing material an agent needs to call this correctly or to relay expectations 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 coverage is 100%, and each of the three properties already documents type, length bounds and meaning (including own_use must be true). The description restates resume_text and job_post as plain text and reinforces own_use, but adds no format, syntax or edge-case detail beyond the schema, 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?

Opens with a specific verb and resource: check your own resume against one job post. It enumerates exactly what comes back (skills/tools/credentials/experience/degree marked required/preferred/mentioned, coverage gaps, repeated words, format problems, signed receipt), so an agent can distinguish this from generic check tools in the sibling list.

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

Usage Guidelines5/5

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

States both the when and the when-not explicitly: 'For the seeker's own resume only: never to rank or reject candidates for an employer,' and requires own_use true as a precondition. It also bounds the scope to one job post and to English, which prevents misuse rather than leaving it to inference.

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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