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Linked API

by io.github.Linked-API
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fetch_job

Read-only

Open a LinkedIn job and retrieve its details such as company, location, salary, and description (st.openJob action). The result carries urn — the permanent LinkedIn job posting URN (urn:li:jobPosting:) — which is null when LinkedIn does not expose the identifier.

Linked API actions are queued into a cloud-browser workflow and may take several minutes. The server returns immediately after starting the workflow with {status: 'pending'|'running', pendingReason, workflowId, operationName, message}. To retrieve the final result, call get_workflow_result with the returned workflowId and operationName — it will long-poll until completion or the request budget elapses, then return either the final result or another in-progress snapshot. Do not retry the original tool while a workflow is still running; that creates duplicate queued work.

A pending workflow carries pendingReason: 'queued' means it is waiting its turn behind other work on the same account and will start within minutes. 'outsideWorkingHours' means the account has configured working hours and the workflow is parked until they reopen — possibly the next working day. In that case get_workflow_result returns immediately instead of polling, message states when the window opens, and you should report that to the user rather than looping.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "jobUrl": "<jobUrl>"
    },
    "name": "fetch_job"
  }
}

Input Schema

NameRequiredDescriptionDefault
jobUrlYesLinkedIn URL of the job. (e.g., 'https://www.linkedin.com/jobs/view/4416248954/')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations only declare readOnlyHint and openWorldHint; the description goes far beyond by disclosing the queued cloud-browser execution model, multi-minute latency, the immediate {status, pendingReason, workflowId, operationName, message} response shape, duplicate-work risk on retry, and the working-hours parking behavior that makes get_workflow_result return immediately. It also flags that 'urn' may be null when LinkedIn doesn't expose the identifier.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose, then the async contract, then the pendingReason branches in priority order. It is long for a one-parameter tool, but nearly every sentence carries operational consequence; the pendingReason paragraph is the only part bordering on verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description does the work of explaining the two-stage return (snapshot then final result) and the nullability of 'urn', which is essential for correct invocation. It stops short of describing error behavior for malformed or inaccessible job URLs, the one remaining gap for a one-param fetch tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the single parameter already carries an example URL, so the description adds nothing about argument format or validation. The mention of 'urn' concerns the return value, not the input, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb and resource ('Open a LinkedIn job and retrieve its details') and enumerates the returned fields (company, location, salary, description), so an agent knows exactly what it does. It also names the underlying action (st.openJob) and the 'urn' output. It doesn't explicitly contrast with the sibling search_jobs, so the search-vs-fetch distinction is only implied by the verb.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Strong guidance on the asynchronous follow-up: call get_workflow_result with the returned workflowId and operationName, do not retry the original tool while a workflow runs, and how to interpret 'queued' vs 'outsideWorkingHours'. What's missing is when to prefer this tool over search_jobs (i.e., you already have a job URL).

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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