Skip to main content
Glama
seinun-ai

Maestro Career Studio

Official
by seinun-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Score Resume Against Job

score_ats

Compute a deterministic hybrid ATS match score for resumes against a job posting, with per-layer breakdowns and rankings to verify edits.

Instructions

Deterministic hybrid match score (no LLM): deterministic lexical layers + anchored pinned-model semantic matching + section-level semantic_fit. Omit target to score ALL base resumes (fast).

target_type "base_resume" (target_id = slug) or "application" (target_id = id).
Same inputs always give the same score; use it to compare bases and to
verify an edit actually moved the number.

Response is {scores, recommendation, countries, next}. `scores` is the raw per-target
list: composite (0-100), per-layer subscores (incl. semantic_fit), gate
warnings, and a per-skill diagnostic table with fix_hints. `recommendation`
is a deterministic ranking of the base-resume rows (order, recommended slug,
margin, close_call, reasons, coverage_warning) — always present, cheap, and
useful during triage even with hints off. Bases set for other countries are
not scored for this job unless `include_other_countries` is true; the
`countries` block ({job_country, fallback, skipped}) names them and is
omitted when a target_id is given. `next` is an optional next-step
hint (null when suppressed or unavailable) naming quick_tailor / create_tailoring_session
against the recommended base.

brief=True suits scoring many jobs in a triage/mass-capture loop: it
suppresses ONLY `next` (never `recommendation`) and is checked before any
settings lookup, so a twenty-posting triage loop pays no extra HTTP
round-trip for a hint nobody will read.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
briefNo
job_idYes
target_idNo
target_typeNo
include_other_countriesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.11.1

TDQS

A4.1/5.0
Behavior3/5

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

Adds real context beyond the annotations: the {scores, recommendation, countries, next} shape, that recommendation is always present even with hints off, that bases for other countries are skipped unless include_other_countries is set, and that brief is checked before any settings lookup. It does not, however, reconcile its determinism/repeatability claim with the readOnlyHint=false and idempotentHint=false annotations, nor explain what side effects (if any) trigger those hints.

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?

Purpose leads, parameter guidance follows, response contract and the brief/triage rationale come last, so it is well front-loaded. Minor waste in the opening redundancy ('Deterministic hybrid match score ... deterministic lexical layers') and the mildly promotional 'always present, cheap, and useful during triage'.

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?

With no output schema, the description correctly takes on return-value explanation and does it in detail (composite, per-layer subscores, gate warnings, diagnostic table, recommendation fields, countries block, next hint). Missing only edge behavior such as invalid job_id or target_id handling and any error surface.

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

Parameters4/5

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

Schema description coverage is 0%, so the description carries the load, and it largely does: target_type semantics with the slug-vs-id distinction, omitting target to score all bases, include_other_countries gating, and brief's exact suppression behavior. Only job_id is left to inference, keeping it short of a 5.

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+resource (score a resume against a job) and pins down the mechanism precisely: deterministic lexical layers plus anchored pinned-model semantic matching, explicitly no LLM. An agent can distinguish this from compare_ats or tailor_session without opening a 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?

Gives concrete when-to-use framing: 'use it to compare bases and to verify an edit actually moved the number' and 'omit target to score ALL base resumes (fast)', plus a triage/mass-capture case for brief=True. It never explicitly excludes a sibling (e.g. compare_ats) or states a when-not-to-use condition, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools