linkedin-agent-mcp

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Get a prepared action

linkedin_get_action

Read-onlyIdempotent

Retrieve the state and exact preview of a prepared action to check confirmation status or after an OUTCOME_UNKNOWN error.

Instructions

[READ - no LinkedIn state is changed] Returns the state and exact preview of a prepared action (local state only). Use it to check whether the user has confirmed an action, or after an OUTCOME_UNKNOWN error.

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": {
      "actionId": "<actionId>"
    },
    "name": "linkedin_get_action"
  }
}

Input Schema

Name	Required	Description	Default
`actionId`	Yes

Output Schema

Name	Required	Description
`type`	Yes
`error`	No
`status`	Yes
`preview`	Yes	Exactly what will happen if the user confirms and the action is executed.
`actionId`	Yes
`createdAt`	Yes
`expiresAt`	Yes
`completedAt`	No
`confirmedAt`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 2, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description still adds value beyond them: it clarifies that only local state is involved and no LinkedIn state is changed, and it names the OUTCOME_UNKNOWN scenario as a reason to call it.

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

Conciseness5/5

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

Three short clauses, zero filler, with the read-only constraint and local-state scope front-loaded before the usage triggers. Every sentence earns its place.

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?

An output schema exists, so return values need not be described, and the description still usefully frames what comes back (state and exact preview). For a one-parameter read tool with full annotations, this is nearly complete; only the actionId sourcing is left implicit.

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?

Single parameter with 0% schema description coverage, so the description carries the burden. It implies actionId refers to a previously 'prepared action', but never explains the id format, length constraints, or where to obtain it (e.g., from linkedin_list_actions output). Minimal viable semantics rather than compensating for the gap.

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?

States a specific verb and resource ('Returns the state and exact preview of a prepared action') and pins the scope as local state only, which separates it from state-changing siblings like linkedin_save_job or linkedin_prepare_save_job. It does not explicitly name the nearby sibling it could be confused with (linkedin_list_actions), so it falls just short of a 5.

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?

Gives two concrete trigger conditions: checking whether a user has confirmed an action, and recovering after an OUTCOME_UNKNOWN error. This is clear when-to-use guidance, but there are no explicit when-not conditions or named alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.