Skip to main content
Glama
lanternrow

linkedin-pages-mcp

by lanternrow

get_share_statistics

Retrieve organic post/share analytics for a LinkedIn Company Page: impressions, clicks, reactions, comments, shares, engagement rate. Aggregate or time-bound; sponsored excluded.

Instructions

Organic post/share analytics for a Company Page: impressions, unique impressions, clicks, reactions, comments, shares, and engagement rate. Aggregate (omit start/end) or time-bound. Sponsored activity is excluded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_msNoRange end, ms since epoch. Omit for a lifetime/aggregate query.
start_msNoRange start, ms since epoch. Omit for a lifetime/aggregate query.
granularityNoTime bucket size for time-bound queries.DAY
organization_idNoNumeric Company Page id. Defaults to LINKEDIN_ORGANIZATION_ID.
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that sponsored activity is excluded, clarifying the 'organic' scope, and explains the aggregate/time-bound behavior. It doesn't mention authentication or side effects, but as a read-only analytics tool, the absence is not a major gap.

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, front-loaded with purpose and metrics, no filler. The description efficiently covers what the tool does and its core usage modes.

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?

For a stats tool with all optional parameters and no output schema, the description covers purpose, metrics, time-bound vs. aggregate behavior, and data exclusions. It does not explicitly describe the response structure beyond listing metrics, but this is an acceptable gap for an agent to select and invoke the tool.

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 schema covers 100% of parameters with descriptions. The description adds only slight clarification that start/end are omitted together for aggregate queries, largely restating schema info. It does not add meaning for granularity or organization_id beyond what the schema provides.

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 returns organic post/share analytics for a Company Page with specific metrics (impressions, clicks, etc.), and explicitly distinguishes itself by excluding sponsored activity. This differentiates it from sibling analytics tools like get_follower_statistics and get_page_statistics.

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 provides clear usage context by explaining the aggregate vs. time-bound modes through omitting or including start/end parameters. However, it does not explicitly name sibling tools or state when to use this tool instead of them, so it lacks explicit alternative guidance.

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

Install Server

Other Tools

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/lanternrow/linkedin-pages-mcp'

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