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keywordise

Jobs that match my CV right now

list_matching_jobs
Read-onlyIdempotent

Live vacancies matched to this account's CV, ranked. Each job carries a calibrated match percentage, a recruiter-pass verdict with a one-line reason, and whether an application was already sent. Filter by match tier and freshness; page with offset.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNofit = best match first; fresh = newest day first, best match within it.fit
freshNonew = posted in the last two weeks; all = the last month.new
limitNo
matchNoMatch tier. 'all' includes weak matches the recruiter pass rejected.good
offsetNo
appliedNohide = leave out jobs already applied to.show

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare this as read-only, idempotent, and non-destructive. The description adds meaningful behavioral context beyond annotations by specifying that results are live and calibrated, and that each job includes a recruiter-pass verdict and application-sent status. No contradiction with annotations is present.

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?

The description is tightly written: three sentences, purpose first, then result contents, then filtering controls. No fluff and no repetition of schema defaults.

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 appropriately lists the key per-job fields and the main filters. It is slightly brief on pagination details and does not mention empty-CV or no-match behavior, but it is sufficient for a read-only list operation in a large sibling set.

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 covers 67% of parameters, and the description restates that filtering is by match tier and freshness and pagination by offset. This adds mild grouping value, but it does not meaningfully enrich beyond the schema's own parameter descriptions for sort, fresh, match, and applied, nor explain limit or offset semantics beyond a brief mention.

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 names a specific verb and resource: listing live vacancies matched to the account's CV, ranked. It also declares what each result contains (match percentage, recruiter-pass verdict, application status), which clearly distinguishes it from siblings like list_my_applications, list_hidden_jobs, or get_job_posting.

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 description gives clear operational context: this tool returns matched vacancies for the current account, filterable by match tier and freshness, with offset pagination. It does not explicitly contrast with alternative tools or state when not to use it, but the intended use case is evident from the description.

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

A3.6/5.0
Disambiguation4/5

Tool purposes are generally distinct and well-described, but a few clusters overlap in function: answer_screening_question vs save_answer both write to the answer book, get_my_profile vs get_account both report plan status, and the CV preview/sent-CV/base-CV tools could be confused. The detailed descriptions mitigate most misselection, so this is only a minor issue.

Naming Consistency4/5

The set almost uniformly uses snake_case verb_noun names like list_, get_, update_, create_, delete_, and start_/stop_. Minor deviations such as login, describe_what_i_want, and the get_my_* vs list_* alternation prevent a perfect score, but the overall pattern is predictable and readable.

Tool Count2/5

49 tools is far above the 25+ threshold and will burden agent tool selection even though many are legitimate single-purpose operations. Several groups could be consolidated—billing links, API-key management, and the CV PDF family—without hurting clarity.

Completeness4/5

The surface covers the full lifecycle: account creation/auth, profile and CV, targeting, matching, apply runs, screening answers, tracking, billing, export, and deletion. Minor gaps remain, such as no application-level detail/withdrawal endpoint and no direct way to save a parsed CV without re-uploading, but agents can work around them.

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