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Magnificent Jobs

by com.magnificentjobs

Search jobs

search_jobs
Read-onlyIdempotent

Search live job postings in the United States (US only — no other countries) by meaning (embedding similarity against the postings). YOU write the expanded query — it is embedded as-is, with no server-side rewriting — so always send query in this shape: ". <One sentence of what the role does; 3-5 key skills/tools>." NO ABBREVIATIONS anywhere in the query — spell everything out (ML → machine learning, AI → artificial intelligence, RN → registered nurse, SWE → software engineer, QA → quality assurance, PM → product manager, CDL → commercial driver's license, EMT → emergency medical technician, etc.) and keep the user's qualifiers (seniority, shift, domain). Example: user says 'ML eng jobs' → query 'Machine Learning Engineer. Builds, trains and deploys machine learning models; Python, PyTorch, MLOps, data pipelines.' Optionally add city (results within radius_miles of that city, ranked by relevance) and/or state. Without a city, ranks across the state or nationwide. Returns job cards with a url to show the user; call get_job for details.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNoUS city name, e.g. 'Denver' or 'Denver, CO'. Non-US cities are not covered and return no match.
limitNoDefault 12.
queryNoExpanded query, embedded verbatim: 'Full Job Title. One sentence of core responsibilities; 3-5 key skills/tools.' Keep user qualifiers (senior, night shift, fintech…). NO abbreviations or acronyms anywhere in the text — always the spelled-out form ('machine learning', not 'ML'; 'registered nurse', not 'RN'; 'software engineer', not 'SWE'); never a single word.
stateNoUS state full name or 2-letter code.
offsetNoFor paging; use nextOffset from a previous call.
radius_milesNo10, 30 or 50 (default 50). Only used with city.
employment_typeNo
experience_levelNoYears-of-experience band.
work_arrangementNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobsYes
modeYesWhich search path ran (city_semantic, city_nearest, state_semantic, nationwide_semantic, newest, …)
countYes
hasMoreYes
resolvedYesHow the request was interpreted: state, city + coordinates, radius, query, filters
nextOffsetYes

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses key behavioral traits: the query is embedded verbatim with no server-side rewriting, ranking depends on city/state inclusion, and results are returned as job cards with a URL. It also instructs how to handle pagination via offset.

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?

The description is longer than strictly necessary, but every section earns its place given the tool's complexity. It is front-loaded with purpose and uses imperative, rule-based language. Minor redundancy exists with the schema's query description (both mention no abbreviations), but overall it remains tight and well-organized.

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?

The description covers all critical aspects: core function, query construction, geographic scope, optional filters, ranking behavior, pagination, output format, and sibling tool direction. Combined with the rich schema and presence of an output schema, it is complete for an AI agent to select and invoke the tool correctly.

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

Parameters5/5

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

The description adds substantial meaning to parameters beyond the schema: it specifies the exact query template, bar on abbreviations, example query, role of city/state, default radius, and how offset pagination works. This goes well beyond the schema's parameter descriptions, especially for `query`.

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?

The description clearly states the tool's function: "Search live job postings in the United States (US only — no other countries) by meaning (embedding similarity against the postings)." This is specific about verb, resource, and scope, and distinguishes it from siblings by noting "call get_job for details."

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?

The description provides explicit when-to-use guidance, including the required query format ("YOU write the expanded query — it is embedded as-is"), geographic constraints, optional filters, and an explicit pointer to an alternative: "call get_job for details." It also explains ranking behavior with and without a city.

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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TDQS

A4.5/5.0
Disambiguation5/5

Each tool has a clear, distinct responsibility: searching jobs, fetching job details, listing states, and finding cities. There is no overlap in purpose, so an agent can easily select the right tool.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern: find_cities, get_job, list_states, search_jobs. This makes the API predictable and easy to work with.

Tool Count5/5

With only 4 tools, the server is tightly scoped for its purpose of job searching and lookup. Each tool earns its place without unnecessary bulk, making it easy for agents to navigate.

Completeness4/5

The core workflow of searching and retrieving jobs is fully covered, supplemented by geographic lookup helpers. A minor gap is the lack of a 'recent jobs' or 'browse all' endpoint, but search functionality mitigates this.

Resources