rate_jobFeedback on one job: hide or dislike removes it from the feed and teaches the matcher what this person rejects; interested is a positive signal. Undo with unhide_job.
| Name | Required | Description | Default |
|---|---|---|---|
| action | Yes | ||
| job_id | Yes | A job_id from list_matching_jobs or list_my_applications. | |
| reason | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds meaningful behavioral detail beyond annotations: hide or dislike removes the job from the feed and trains the matcher, interested is a positive signal, and the action can be undone via unhide_job. This gives the agent a clear picture of side effects and reversibility beyond the readOnly/destructive hints.
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?
Two concise sentences cover the core action, behavioral consequences, and the undo path. It is front-loaded with the primary purpose and contains no filler or redundancy.
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?
The description is complete enough for a simple feedback tool: it covers the action semantics, feed impact, matcher signal, and reversibility. The only notable gap is the undocumented reason parameter, but the tool is otherwise straightforward and no output schema adds complexity.
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?
Schema description coverage is only 33%, so the description has to compensate for the action and reason parameters. It does explain the meaning of the action values (hide/dislike vs interested), which helps. However, it does not explain the optional reason parameter at all, leaving its purpose and formatting unclear.
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 a specific operation (feedback on one job), defines what each action does, and names the related undo tool unhide_job. It clearly distinguishes this tool from siblings like list_hidden_jobs or unhide_job by describing the feedback mechanism rather than just restating the title.
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 states when to use the tool: when the user wants to give feedback on a single job, with hide/dislike for rejection and interested for positive signal. It also routes undo behavior to unhide_job. It does not explicitly list exclusions or alternatives beyond the undo case, but the context is clear enough for an agent to select it appropriately.
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.