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himanshu31shr

LinkedIn MCP Server

get_org_share_statistics

Retrieve engagement statistics for LinkedIn organization posts, optionally filtered by specific share IDs, to analyze post performance and audience interaction.

Instructions

Get share/post engagement statistics for a LinkedIn organization

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
organizationIdYesThe numeric ID of the organization
shareUrnsNoOptional list of share URNs to filter statistics by
Behavior2/5

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

With no annotations, the description carries full responsibility for behavioral disclosure. It only states 'gets' statistics, offering no details on data freshness, authentication requirements, rate limits, or response format (e.g., aggregated vs. per-share). This is insufficient for a no-annotation tool.

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 sentence of 10 words, starting with the action verb 'Get'. It is perfectly concise, with no unnecessary content.

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

Completeness3/5

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

Given the tool's simplicity (2 parameters, no output schema), the description is adequate for a basic get operation. However, it does not explain the response structure (e.g., what engagement metrics are included: likes, comments, shares counts), which leaves the agent partially guessing. There is scope for more completeness.

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?

Schema description coverage is 100%, with parameter descriptions like 'The numeric ID of the organization' and 'Optional list of share URNs to filter statistics by'. The tool description adds no additional meaning, so it meets the baseline of 3 for high-coverage schemas.

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 clearly states it gets 'share/post engagement statistics for a LinkedIn organization', specifying the verb and resource. It distinguishes from siblings like get_org_follower_statistics and get_org_page_statistics by focusing on share/post engagement, but does not explicitly differentiate from get_org_posts which might also retrieve posts (though likely without engagement metrics). Overall, it is clear and specific.

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?

The description provides no guidance on when to use this tool versus alternatives (e.g., get_org_posts, get_org_page_statistics). It does not mention prerequisites, context, or conditions, leaving the agent without decision-making support.

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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