Retrieve a specific Google job posting by its numeric ID. Returns the parsed career page details from Google Careers, giving clean job data.
Google Jobs single posting. Returns one Google Careers posting by its numeric job id (the id field returned by search). Parsed from careers.google.com's server-rendered job detail page.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Numeric Google job id |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the data source ('Parsed from careers.google.com's server-rendered job detail page') and implies a read-only operation via 'Returns.' However, it does not mention potential issues like scraping limitations, latency, or failure modes. For a simple read tool this is adequate but not rich, earning a mid-range score.
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 three concise sentences, front-loaded with the primary purpose. Each sentence adds necessary detail: what it does, how to identify the posting, and the source page. No wasted words or repetition.
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 tool with one parameter and no output schema, the description is mostly complete. It states the return (a posting), the identifier, and the source. It does not detail the fields in the returned posting, but given the simplicity and the absence of an output schema, this is a minor gap. The description adequately covers the essential context.
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 schema already describes `id` as 'Numeric Google job id' (100% coverage), so the baseline is 3. The description adds value by linking the parameter to the search result (`the `id` field returned by search`), clarifying which ID to use and reinforcing the parameter's origin. This semantic enrichment justifies a 4.
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 clearly states what the tool does: 'Returns one Google Careers posting by its numeric job id.' It specifies the resource (Google Careers posting), the verb (returns), and the identifier (id from search), which distinguishes it from search tools like google_jobs_search. The source ('Parsed from careers.google.com's server-rendered job detail page') adds further specificity, making the purpose unambiguous.
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 clear context on when to use this tool: it takes the `id` field returned by a search, implying it should be used after google_jobs_search. However, it does not explicitly state when not to use it or name alternative tools, so it stops short of a full 5. The mention of 'single posting' and the id origin provides usable guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.