Fresh Linkedin Profile Data MCP Server

Get Posts Reactions

get_posts_reactions

Retrieve reactions from a LinkedIn post by its URN. Filter by reaction type and paginate results to analyze engagement.

Instructions

1 credit per call.

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": {
      "urn": "<urn>"
    },
    "name": "get_posts_reactions"
  }
}

Input Schema

Name	Required	Description
`urn`	Yes	Example value: 7267273010393358336
`page`	No	Example value: 1
`type`	No	Default value: ALL. Possible values: ALL,LIKE,EMPATHY,APPRECIATION,INTEREST,PRAISE.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Sep 4, 2026

TDQS

D1.4/5.0

Behavior1/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions only credit cost and says nothing about side effects, pagination behavior, filtering semantics, response characteristics, or authorization needs. This is almost entirely non-disclosure of behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The text is extremely short, but brevity here is under-specification rather than conciseness. A single cost statement does not constitute a useful tool description and leaves nearly all functional information absent.

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

Completeness1/5

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

The tool has no annotations and no output schema, yet the description provides no functional context whatsoever. It fails to explain what the tool returns, how pagination works, what 'type' means, or why this tool differs from many related post- and reaction-related siblings. The definition is completely inadequate for correct invocation.

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 input schema has 100% description coverage, with each parameter providing an example or default value. The description adds no parameter-level meaning, but the schema is sufficient to understand the parameters' roles. Baseline of 3 is appropriate since the schema carries the weight.

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

Purpose1/5

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

The description is only '1 credit per call.' It does not state what the tool does, what resource it acts on, or what operation it performs. The name and title imply 'get reactions for posts,' but the description itself is functionally empty and misleading as a purpose statement.

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

Usage Guidelines1/5

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

There is no guidance on when to use this tool versus alternatives like get_posts_comments, get_post_details, or get_profiles_posts. No use cases, prerequisites, or exclusions are provided. An agent has no basis for selecting this tool over its siblings.

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

Deploy Server