Skip to main content
Glama
oliverhruby

LinkedIn MCP Server

by oliverhruby

list_campaigns

Retrieve ad campaigns from LinkedIn using query parameters to inspect and manage campaign data.

Instructions

List ad campaigns.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNo/rest/adCampaigns
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 no annotations, the description carries the full behavioral burden, but it only says 'List,' implying a read operation. It does not disclose authentication needs, pagination behavior, filtering semantics, or any side effects. The output schema helps with return shape, but behavioral expectations are largely unspecified.

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?

A single short sentence with no filler. The core action and resource are front-loaded, and every word earns its place.

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?

For a tool with no annotations, undocumented parameters, and a nontrivial query_json input, the description is too thin. The output schema provides return structure, but the agent still lacks enough context to know how to construct a valid request or when this tool is the right choice.

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

Parameters1/5

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, but it adds nothing about how 'path' or 'query_json' should be used. The defaults suggest an endpoint path and a JSON query, yet no explanation, examples, or parameter-level semantics are provided anywhere.

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: 'List ad campaigns.' It clearly distinguishes from create/update/delete campaign operations and from list_campaign_groups, though the distinction from campaign groups is only implicit in the noun 'campaigns' versus 'campaign groups.'

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 provided on when to use this tool versus alternatives like list_campaign_groups or get_ad_analytics. There are no stated exclusions, prerequisites, or conditions that would help an agent choose it over sibling tools.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/oliverhruby/linkedin-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server