get_targetingEvery targeting rule the feed and the apply run obey: remote mode, geo scope and countries, locations, include/exclude keywords, title allow/block lists, seniority, salary, languages, work authorisation, company allow/block, daily and per-company caps, cover-letter and CV settings, screening defaults.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey read-only, idempotent, non-destructive behavior. The description adds useful context about what the targeting rules govern (feed and apply runs), but it does not disclose return format, ordering, or how the settings are presented. This is a reasonable midpoint given annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense, comma-separated enumeration of many settings. It is information-packed and front-loads the key phrase 'Every targeting rule the feed and the apply run obey', but the long list is harder to scan than a structured or summarized version.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless read-only getter, the description covers the substance of what will be returned: all targeting rule categories. Annotations cover the safety model, and there is no output schema or parameter complexity requiring further explanation. The main gap is that it never explicitly states it returns the current saved configuration.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and 100% schema coverage, so the description cannot meaningfully add parameter-level detail. The no-parameter baseline is 4, and the description appropriately focuses on clarifying the scope of the returned resource instead.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the resource as 'every targeting rule the feed and the apply run obey' and enumerates concrete categories (geo, keywords, seniority, salary, etc.), which clearly distinguishes it from sibling update_targeting. However, it lacks an explicit retrieval verb like 'returns' or 'gets', so it leans on the tool name for the actual action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus alternatives. It implies read access to current targeting settings but never states 'use this to inspect current targeting before changing anything' or names update_targeting as the write counterpart. Prerequisites and use cases are absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.