Fetch a single Tesla Careers job posting by its numeric job ID. Use the ID returned by the list endpoint to retrieve parsed job details as JSON.
Tesla Jobs single posting. Returns one Tesla Careers posting by its numeric job id (the id field returned by the list endpoint). Parsed from tesla.com's own job detail JSON endpoint.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Tesla job id |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the transparency burden. It usefully discloses that data is parsed from Tesla's own job detail JSON endpoint, implying a direct scrape of Tesla's site, but it does not describe the response structure, fields, or any potential limitations such as rate limits or authentication needs.
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 short, front-loaded sentences with no wasted words. Each sentence adds relevant information: what it does, how the id is obtained, and where the data comes from.
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 one-parameter fetch tool, the description covers the essential context: target resource, id source, and data origin. The lack of an output schema is partially mitigated by the clear statement that it returns 'one Tesla Careers posting', though a bit more detail about the returned fields would make it fully complete.
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 documents the `id` parameter with 100% coverage, so the baseline is 3. The description adds meaningful detail by clarifying that the id is numeric and comes from the list endpoint, which helps the agent understand the expected value beyond the minimal schema description.
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 the tool returns one Tesla Careers posting by its numeric job id, making the purpose specific and unambiguous. It also distinguishes this tool from the list/search job tools by referencing the `id` field from the list endpoint.
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 implies the intended workflow: first use the list endpoint to obtain an `id`, then call this tool. It does not explicitly name alternatives or state when not to use, 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.