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Datamagnet

by co.datamagnet
OverviewDiscussionsAdmin

Get post engagement

get_post_engagement
Read-onlyIdempotent

Fetch the people who engaged with a LinkedIn post, with their profiles. kind picks which kind of engagement:

  • comments — commenter profiles, comment text and reply threads

  • reactions — who reacted and with which reaction type

  • reposts — who reshared it and any commentary they added

Costs 10 credits per page, so paginate deliberately. Ask for page 1 first and only go further if the user needs more than it returns.

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": {
      "url": "<url>",
      "kind": "<kind>"
    },
    "name": "get_post_engagement"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYes
kindYes
pageNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, open-world behavior. The description adds important context beyond that: each page costs 10 credits and pagination should be deliberate, which materially affects agent behavior.

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?

The description is front-loaded, tightly structured, and uses bullets only where they add clarity. Every sentence contributes purpose, parameter meaning, or cost/pagination guidance.

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?

Given annotations and an output schema, the description covers the key invocation decisions: what each `kind` returns, that profiles are included, and pagination cost. Minor details such as exact URL format or auth constraints are not stated, but the core call context is complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must carry parameter meaning. It explains `kind` thoroughly and gives pagination guidance for `page`, while `url` is strongly implied as the LinkedIn post URL by the opening sentence.

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 states a specific verb and resource: fetch the people who engaged with a LinkedIn post, including their profiles. The three engagement kinds make the scope unambiguous and distinguish it from post-content or profile-only siblings.

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 gives clear operational guidance for choosing a `kind` and for pagination: start with page 1 and only continue if the user needs more. It does not name an alternative sibling tool or when-not to use this one, so it stops short of full alternative routing.

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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