explain_job_matchFor one vacancy: the match percentage, the requirements this account's CV covers and the ones it is missing. With narrative=true, also a short written assessment with strengths and gaps (a paid model call; limited to 60 per hour).
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | A job_id from list_matching_jobs or list_my_applications. | |
| narrative | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that narrative=true triggers a paid model call with a 60-per-hour limit, which is a real behavioral consequence not available in annotations. Combined with readOnlyHint, idempotentHint, and destructiveHint, the agent understands this is a safe but potentially costly operation.
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 well-structured sentence front-loads the scope and packs only essential details. The parenthetical about the paid model call and rate limit is high-value information stated without waste.
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 two-parameter read-only tool with no output schema, the description enumerates all returned components: match percentage, covered requirements, missing requirements, and optional narrative assessment. No essential calling information is missing.
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?
narrative has no schema description, but the tool description fully explains its meaning, when to enable it, and its cost/quota. The other parameter, job_id, is already well-described in the input schema.
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?
States the tool analyzes one vacancy and returns match percentage plus covered and missing requirements, optionally adding a written assessment. This clearly distinguishes it from sibling tools by its per-vacancy focus and explicit output elements.
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 opening 'For one vacancy' gives clear context for when to use the tool, and the schema links job_id to list_matching_jobs or list_my_applications. It does not explicitly name alternatives or exclusions, but the usage context is otherwise clear.
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.