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thenavidm

ScrapeCreators MCP Server

by thenavidm

Post

linkedin_post

Fetches a LinkedIn post or article with full text, author details, engagement metrics, comments, and related articles; confirm=true for paid calls.

Instructions

Fetches a single LinkedIn post or article, returning the title, headline, full description text, author info with follower count, publication date, like count (reactions), comment count, and individual comments. For public feed posts, activityUrn and contentUrn expose LinkedIn's public activity and underlying share or ugcPost URNs when present; either can be null when LinkedIn does not expose it. Also includes related articles from the same author in moreArticles. Potentially consumes paid API credits; requires confirm=true. Read-like POST requests do not publish to social platforms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe URL of the LinkedIn post to get
accountNoNamed private ScrapeCreators account; selects credentials, not a remote account ID.
confirmNoMust be true for the specific approved credit-consuming research call.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations declare readOnlyHint=false and destructiveHint=false, but leave ambiguous whether a POST has side effects. The description resolves this ('Read-like POST requests do not publish to social platforms') and adds credit consumption plus the confirm=true gate – real behavioral context beyond the annotations. It stops short of describing pagination or comment-volume limits.

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

Conciseness4/5

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

Front-loaded with the core action and returned fields, then the URN caveat and the cost/confirm constraint. The URN sentence is dense and somewhat niche, but every sentence carries usable information.

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?

With no output schema, the description correctly carries the burden of enumerating return fields (title, author, reactions, comments, moreArticles), and it covers the credit/confirm constraint. Only the response shape for edge cases (null URNs is covered; error behavior is not) remains thin.

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%, so all three parameters are already documented. The description reinforces confirm=true and clarifies the account credential scoping indirectly, but adds no syntax or format detail beyond the schema. Baseline 3 applies.

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?

States a specific verb (fetches) and resource (a single LinkedIn post or article), and enumerates the returned payload. The 'single' framing cleanly separates it from linkedin_search_posts, linkedin_company_posts, and linkedin_post_transcript without naming them.

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

Usage Guidelines3/5

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

The confirm=true requirement and the credit-cost warning give real invocation context, and the URL parameter implies the use case. But it never says explicitly when to pick this over linkedin_post_transcript or linkedin_search_posts, nor what happens if confirm is omitted.

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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