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linkedin-mcp-pro

linkedin-mcp-pro

by horizonbymuneeb
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

react_to_post

Add a reaction such as Like, Celebrate, or Insightful to a LinkedIn post using its URL or URN.

Instructions

Add a reaction (like, celebrate, insightful, etc.) to a post.

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": {
      "target": "<target>"
    },
    "name": "react_to_post"
  }
}

Input Schema

NameRequiredDescriptionDefault
targetYesLinkedIn post URL OR URN
reaction_typeNoLIKE
dry_runNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations provided, so the description must convey behavioral traits. It fails to mention mutability (e.g., undo or overwrite reactions), rate limits, or the effect of dry_run. The minimal description does not compensate for the lack of annotations.

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

Conciseness3/5

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

The description is a single sentence, which is concise, but it lacks necessary detail. While front-loaded with the action, it is too sparse for a tool with multiple parameters and behavioral implications.

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?

Given the simple tool, no output schema, and low schema coverage, the description is incomplete. It omits return values, authentication requirements, error scenarios, and outcome of the dry_run parameter.

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 coverage is low (33%) and the tool description adds no extra meaning beyond the schema. The description does not clarify parameter details, defaults, or interactions, leaving the agent to rely solely on schema fields.

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's action ('Add a reaction') and resource ('to a post'), and mentions example reaction types. It effectively distinguishes from sibling tools like comment_on_post or create_post.

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?

No guidance on when to use this tool versus alternatives. It doesn't specify prerequisites, limitations, or exclusions, leaving the agent to infer context.

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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