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LinkedIn Ads MCP by InsightfulPipe

by com.insightfulpipe
OverviewDiscussionsAdmin

LinkedIn Ads Data Query

query_data
Read-only

Query LinkedIn Ads data (Step 3 (FINAL) of workflow).

REQUIRED: Complete these steps first using query_contexts:

  1. request='accounts' - Get workspace_id, brand_id, ad_account_id AND available_actions

  2. request='actions_details' with actions=['xxxxxx', 'xxxxxx', 'xxxxxx'] - Get detailed body structure for specific action(s) using actions from available_actions

IMPORTANT: Always call step 2 to see the exact body structure before querying.

Rely on info from steps 1-2 to construct the query body. Follow the schema exactly.

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": {
      "body": "<body>"
    },
    "name": "query_data"
  }
}

Input Schema

NameRequiredDescriptionDefault
bodyYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, and the description adds important behavioral constraints: this is the final workflow step and the body must be constructed using information from prior query_contexts calls. It does not contradict annotations and adds meaningful process context beyond them.

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?

The description is front-loaded with the workflow and structured into required steps, which suits the tool's complexity. There is some repetition around always calling step 2 and relying on steps 1-2, but it remains focused.

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 and annotations cover the safety profile, so the description does not need to explain return values or read-only behavior. It adequately explains how to obtain the opaque body structure, though it could better distinguish this query tool from execute_action.

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?

The only parameter is body, and schema description coverage is 0%, so the schema provides essentially no field-level guidance. The description compensates somewhat by directing the agent to query_contexts actions_details for the exact body structure and to follow the schema exactly, but it does not define body fields itself.

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 states a specific verb and resource: query LinkedIn Ads data, and identifies it as the final step of a workflow. It is clear what the tool does, though it does not explicitly differentiate query_data from the sibling execute_action.

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?

It gives explicit prerequisite steps and ordering: first call query_contexts with request='accounts', then request='actions_details', and always call step 2 before querying. It lacks a direct when-not-to-use or comparison against execute_action, but the required workflow context is unusually clear.

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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