ContentIn — LinkedIn Ghostwriter

Get post analytics

get_post_analytics

Read-onlyIdempotent

Get LinkedIn performance for one published ContentIn post: impressions, members reached, likes, comments, shares, and a derived engagement rate. IMPORTANT: metrics are fetched on a schedule and only for posts published through a connected LinkedIn account, so a post can legitimately have no numbers yet. When that happens this returns measured: false — report that honestly as 'not measured yet'. Do NOT describe an unmeasured post as having zero impressions or zero engagement; those are different claims and only one of them is true.

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": {
      "post_id": "<post_id>"
    },
    "name": "get_post_analytics"
  }
}

Input Schema

Name	Required	Description	Default
`post_id`	Yes	The ContentIn post id, from list_posts.
`include_history`	No	Include the metric time-series (up to 60 snapshots) instead of just the latest figures. Default false.

Schema Changelog

Changes observed during successful MCP inspections.

First observedAug 18, 2026

TDQS

A4.7/5.0

Behavior4/5

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

The description adds significant behavioral details beyond the annotations: it explains that metrics may be unavailable and how to interpret the 'measured: false' response. This clarifies a non-obvious behavior. While annotations already indicate readOnlyHint and idempotentHint, the description enhances transparency for this specific edge case. A slight deduction because it doesn't explicitly confirm that the tool is non-destructive, but that is already covered by annotations.

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?

The description is efficient, front-loading the core purpose in the opening sentence and using subsequent sentences for critical caveats. Every sentence serves a distinct purpose: purpose, context about data availability, and usage instructions for the edge case. There is no fluff.

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

Completeness5/5

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

Given that the schema covers all parameters, annotations cover the behavioral safety profile, and there is no output schema, the description provides complete contextual information for an AI to invoke the tool correctly. It explains when data might not be available and how to report it, which is a critical and non-obvious requirement. No gaps remain.

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

Parameters4/5

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

Schema coverage is 100%, so the schema already documents both parameters well. The description adds minimal parameter-specific details but provides broader context about the tool's behavior. The key metric list helps agents understand what 'analytics' includes. However, it doesn't elaborate on the 'include_history' parameter beyond what the schema provides, which is why this isn't a 5.

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

Purpose5/5

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

The description clearly states the verb ('get'), resource ('LinkedIn performance for one published ContentIn post'), and lists the specific metrics returned ('impressions, members reached, likes, comments, shares, and a derived engagement rate'). It distinguishes itself from sibling tools like 'list_posts' or 'publish_post' by focusing on analytics retrieval.

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

Usage Guidelines5/5

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

The description explicitly tells the AI when to use the tool ('for one published ContentIn post') and important context about when it should not be expected to return data ('metrics are fetched on a schedule and only for posts published through a connected LinkedIn account'). It provides guidance on handling a specific edge case (returning 'measured: false' and not incorrectly reporting zeros).

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

All ContentIn — LinkedIn Ghostwriter tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?