get_job_postingThe full description of one matched vacancy, with a short summary and highlights, plus the link that opens the posting from the app.
| 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 declare readOnlyHint, idempotentHint, and destructiveHint, so the operational safety is covered. The description adds value by disclosing the response contents (full description, summary, highlights, link), which is particularly useful given there is no output schema.
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?
One clean sentence that front-loads the core purpose and lists the included elements. No filler, repetition, or ambiguity.
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 simple read-one operation with a single required parameter, rich safety annotations, and a description that enumerates the returned fields, the definition is complete. The source of job_id is provided in the schema, so an agent has everything needed to call it correctly.
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 single parameter job_id is fully documented in the schema with a helpful provenance note ('A job_id from list_matching_jobs or list_my_applications'). Schema coverage is 100%, so the description does not need to add parameter semantics; it correctly stays silent.
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 title 'Read one job posting' and description 'The full description of one matched vacancy' clearly identify a read operation for a single job posting. It is distinguishable from list_matching_jobs (which lists jobs) and explain_job_match (which explains matches), though sibling differentiation is not explicit.
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?
Usage is implied through the parameter description, which states the job_id must come from list_matching_jobs or list_my_applications. However, the description does not explicitly state when to use this tool rather than alternatives like explain_job_match or get_match_status, nor does it mention exclusions.
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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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.