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dubaikseniia-wq

entra-mcp

by dubaikseniia-wq
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Match a candidate to jobs

match_jobs
Read-onlyIdempotent

Match resumes or skill sets to verified tech and AI jobs, receiving ranked results with explainable fit scores, skill gaps, salary data, and interview chance hints.

Instructions

AI matching, live: pass resume_text (free text) or skills[] plus optional target_role / remote_only / country / min_salary. Extracts skills, role and seniority deterministically, runs 2–4 searches, dedupes and ranks with an explainable fit_score (0–100), why[], matched_skills, skill_gaps, salary and a qualitative interview_chance_hint. ENTRA finds and ranks; the human applies.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax matches (default 10, max 20)
skillsNoKey skills if you have no resume text, e.g. ["python","llm","rag"]
countryNoISO-2 country code or country name
min_salaryNoMinimum ANNUAL salary in USD
remote_onlyNoOnly remote roles
resume_textNoFree-text resume / profile (≤8000 chars). Skills, role and seniority are extracted deterministically.
target_roleNoTarget role, e.g. "machine learning engineer" (overrides the role detected in resume_text)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already mark it read-only/idempotent, and the description adds substantial behavior: deterministic extraction, 2–4 searches, dedup, explainable fit_score, why[], matched_skills, skill_gaps, salary, interview_chance_hint, and the note that the human applies. This goes well beyond the structured annotations.

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

Conciseness5/5

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

Three high-signal sentences: inputs are front-loaded, behavior and output are compactly specified, and the final sentence conveys the no-application side effect. No filler.

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 explains the main return concepts (fit_score, why[], matched_skills, skill_gaps, salary, interview_chance_hint) and the matching pipeline, so an agent knows what to expect and what it can do with the result.

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% with detailed parameter descriptions, so the baseline is 3. The description reiterates which parameters are optional but does not add materially new semantics beyond the schema.

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

Purpose4/5

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

The description clearly states the verb ('match'), resource ('jobs'), and input modes (resume_text or skills[]), and lists the ranking output (fit_score 0–100). It does not explicitly contrast itself with match_profile or search_jobs, so it misses the sibling-differentiation bar for a 5.

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?

The opening sentence gives concrete usage context: provide resume_text or skills[], plus optional filters, and it will run searches and rank matches. It does not name alternatives or state when not to use it, but the usage context is clear enough for 4.

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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