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oliverhruby

LinkedIn MCP Server

by oliverhruby

get_ad_analytics

Retrieve LinkedIn ad analytics by submitting a custom query payload to return performance metrics for campaigns.

Instructions

Fetch ad analytics with custom query payload.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNo/rest/adAnalytics
query_jsonNo{}

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

C2.7/5.0
Behavior2/5

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

With zero annotations, the description carries the full behavioral disclosure burden. The verb 'Fetch' implies a read-only operation, which is the only behavioral signal; nothing is said about authentication requirements, error behavior, pagination, or constraints on the query payload. The output schema covers return shape, but operational behavior remains largely undisclosed.

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?

A single, front-loaded sentence with no wasted words; the verb and resource appear immediately. The vague phrase 'custom query payload' costs a point, but the description is efficiently compact for what it does convey.

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

Completeness2/5

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

Although the tool is structurally simple (two optional parameters with defaults and an output schema), the description leaves the core semantics undefined: what analytics are returned, what a valid query payload contains, and whether authentication is required. An agent could invoke it with defaults but cannot predict the meaning or scope of the result.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for the two undocumented parameters. It adds only that query_json is a 'custom query payload,' leaving its expected format and supported fields unexplained, and says nothing about the path parameter's role or how the two parameters interact.

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 ('Fetch') and resource ('ad analytics'), making the core operation clear. The phrase 'with custom query payload' adds the invocation mechanism, but it does not explicitly differentiate this tool from siblings like list_campaigns or list_ad_accounts.

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

Usage Guidelines2/5

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

No guidance is given on when to use this tool versus sibling tools such as list_campaigns, list_ad_accounts, or any auth_* tool. There is no stated prerequisite, no alternative-selection condition, and no mention of when this tool should not be used.

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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