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Score my CV against a job

match_cv
Read-onlyIdempotent

Requires techjobs.vn sign-in. Scores how well the signed-in user's primary CV fits one open job (0–100, rule-based), with the required skills they match and miss. Limited to 30 matches per day per account, so use it for a job the user names; score_cv rates the CV itself and suggest_jobs_for_me finds jobs that fit it.

Input Schema

NameRequiredDescriptionDefault
job_idYesJob id from search_jobs, get_job or suggest_jobs_for_me results.

Output Schema

NameRequiredDescriptionDefault
jobYes
scoreYesRule-based fit, 0–100.
reasonsYes
primary_cvYesThe CV chính that was scored: the one CV used for matching and referrals.
detailed_review_urlYesJob page tab where the user can ask for a detailed AI review.
matched_must_have_skillsYes
missing_must_have_skillsYes
matched_nice_to_have_skillsYes
missing_nice_to_have_skillsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, closed-world and non-destructive, so the safety profile is covered. The description adds context annotations cannot: a techjobs.vn sign-in prerequisite, a per-account rate limit of 30 matches/day, and confirmation that scoring is rule-based and returns matched vs. missed required skills. Return-shape details are legitimately omitted because an output schema exists.

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?

Three dense sentences with no filler: prerequisite, what it does and returns, then quota plus sibling routing. The order is logical, though the quota and alternatives are packed into one long clause that an agent must parse rather than skim.

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?

For a one-parameter, read-only scoring tool with an output schema, everything an agent needs is present: auth requirement, quota, score semantics, and explicit disambiguation from the two nearest siblings. Nothing material is left to inference.

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% and the single job_id parameter is already documented as coming from search_jobs, get_job or suggest_jobs_for_me results. The description adds only the implication that the job must be open and that the primary CV (not a variant) is used, so the baseline 3 is appropriate.

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: scores the signed-in user's primary CV against one open job, with a 0–100 rule-based scale and matched/missed required skills as output. It explicitly distinguishes itself from score_cv (rates the CV itself) and suggest_jobs_for_me (finds fitting jobs), so an agent can route correctly without opening any schema.

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?

Gives a concrete when-to-use condition ('a job the user names') and names both alternatives with the case each covers. The 30-per-day quota is framed as the reason to be selective, which is usage guidance rather than just trivia.

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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