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entra-mcp

Search ENTRA jobs

search_jobs
Read-onlyIdempotent

Search verified AI and tech jobs aggregated from company hiring systems. Filter by location, salary, experience, or posting date to see live roles with salary bands and apply links.

Instructions

Search thousands of verified AI & tech jobs on ENTRA (aggregated directly from company hiring systems — zero ghost jobs). Filters: work location, country, company, annual salary, experience, posted_within_days. rank_by date | salary | fit (fit needs profile_keywords). Returns total_found and live roles with salary bands and apply links.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results (default 10, max 50)
queryYesFree-text search, e.g. "machine learning engineer"
remoteNoShorthand for work_location="remote"
companyNoCompany slug or name, e.g. "openai" or "Stripe"
countryNoISO-2 country code (US, GB, DE, AE…) or country name
rank_byNodate (default) · salary (listed bands only, high→low) · fit (needs profile_keywords)
experienceNoExperience level
salary_minNoMinimum ANNUAL salary (default currency USD; ENTRA converts other currencies)
work_locationNoFilter by work location
sort_by_salaryNoDeprecated — use rank_by="salary"
salary_currencyNoCurrency for salary_min (default USD)
profile_keywordsNoCandidate skills for rank_by="fit", e.g. ["python","pytorch","llm"]
posted_within_daysNoOnly roles published in the last N days

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already establish the safety profile (readOnly, idempotent, openWorld). The description adds useful behavior beyond that: data origin (company hiring systems, zero ghost jobs), and what is returned (total_found, live roles with salary bands and apply links). This helps an agent anticipate output shape without an output schema.

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 dense sentences front-load the core promise, then compactly list filters, sorting behavior, and return info. No wasted words; all sentences carry distinct value for an agent deciding whether and how to call the tool.

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?

For a read-only search tool with 13 well-documented parameters and no output schema, the description covers the main filters, sort options, a parameter dependency, and return summary. It could mention pagination limits or the remote shorthand, but the schema already covers those details, so nothing critical 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 parameters are fully documented in structured form. The description summarizes key filters (work location, country, company, salary, experience, posted_within_days) and clarifies the fit dependency, but adds little semantic detail 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?

Description opens with a specific verb and resource: 'Search thousands of verified AI & tech jobs on ENTRA'. It clearly states the tool's function and main selling points (verified, aggregated from company systems). However, it does not distinguish itself from siblings like match_jobs, so it misses the top score.

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

Usage Guidelines3/5

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

The description provides practical how-to guidance by listing available filters and explaining rank_by behavior, including the prerequisite that fit needs profile_keywords. It does not, however, state when to choose this tool over siblings such as match_jobs or get_job, so usage context is implied rather than explicit.

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