unhide_jobUndo hide or dislike: the job returns to the feed and the negative signal is dropped.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | A job_id from list_matching_jobs or list_my_applications. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already signal a non-read-only mutation (readOnlyHint=false) and non-idempotency. The description adds valuable behavioral detail beyond that: it states the job returns to the feed and the negative signal is dropped. This clarifies side effects without contradicting the annotations.
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?
A single, front-loaded sentence states the action and its consequences with no filler. Every phrase earns its place, and the key behavior is immediately visible.
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 one-parameter mutation with annotations covering read-only/idempotent/destructive aspects, the description is essentially complete. It explains the effect and the input source is covered by the schema. A small gap is the lack of any expected response or error semantics, but this is minor given the tool's simplicity and low parameter count.
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 input schema fully documents job_id with its allowed source, and there is only one parameter. The description adds no additional parameter-level meaning beyond referring to 'the job', so the schema carries the burden and the baseline of 3 is appropriate.
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 opens with a specific verb and target ('Undo hide or dislike') and clearly distinguishes this from read-only siblings like list_hidden_jobs by stating the job returns to the feed. The title reinforces the same meaning, so an agent can immediately tell what this tool does.
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 makes the operative scenario explicit: use this when a job was hidden or disliked and should be restored to the feed. It does not name alternative tools explicitly or state when not to use it, but the use case is clear enough and the sibling set contains no equivalent 'hide' tool.
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.