mcp-server-linkedin

linkedin_get_all_stats

Retrieve aggregated analytics across all LinkedIn posts, including total impressions, clicks, likes, comments, and shares. Requires Community Management API access.

Instructions

Get aggregated analytics across ALL LinkedIn posts (total impressions, clicks, likes, comments, shares). NOTE: Currently requires Community Management API access (r_member_postAnalytics scope) which must be applied for separately. Returns instructions on how to gain access if not available.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "linkedin_get_all_stats"
  }
}

Input Schema

Name	Required	Default
`metric`	No	impressions
`end_date`	No
`start_date`	No
`aggregation`	No	daily

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Jul 28, 2026

TDQS

A4.1/5.0

Behavior4/5

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

With no annotations, the description adds value by disclosing the access requirement and the tool's behavior when access is missing (returns instructions). It does not cover other traits like rate limits or data freshness, but the access context is critical.

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?

Two concise sentences: first states purpose, second covers prerequisite and fallback behavior. No unnecessary words, front-loaded with key information.

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?

Despite having an output schema, the description omits parameter details entirely. For a tool with 0% parameter documentation, the description is incomplete for an agent to invoke correctly without additional lookup.

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%, and the description does not explain any of the four parameters (metric, start_date, end_date, aggregation). The purpose mentions metrics but not how to use the parameters. The description adds no semantic value beyond the schema's basic titles.

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 tool aggregates analytics across ALL LinkedIn posts, listing specific metrics (impressions, clicks, likes, comments, shares). This distinguishes it from linkedin_get_post_stats, which is for individual posts.

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?

Explicitly notes the required Community Management API access and scope, and describes behavior when access is unavailable (returns instructions). This guides the agent on prerequisites and fallback.

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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