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thenavidm

ScrapeCreators MCP Server

by thenavidm

Company Posts

linkedin_company_posts

Retrieve paginated posts from a LinkedIn company page, including each post's URL, ID, date, and full text. Supports up to 7 pages; requires confirmation for paid API credits.

Instructions

Retrieves paginated posts from a LinkedIn company page, including each post's URL, ID, publication date, and full text content. Supports page-based pagination up to a maximum of 7 pages due to a LinkedIn platform limitation. 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 Read-like POST requests do not publish to social platforms.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 company page to get
pageNoThe page number 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

A4/5.0
Behavior4/5

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

Annotations declare readOnlyHint=false, but the description adds material context beyond them: it consumes paid API credits, needs confirm=true, caps at 7 pages due to a platform limit, and clarifies that the read-like POST does not publish to social platforms. This resolves the non-obvious readOnlyHint=false flag, which is genuinely useful. It stops short of describing error or rate-limit behavior beyond the page cap.

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?

Three tight sentences, front-loaded with the core action and return shape, then constraints, then the cost/confirm caveat. No filler.

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?

No output schema exists, but the description enumerates the returned fields and covers cost, confirmation, and pagination limits, which is enough for correct invocation. Minor gaps remain around error behavior and what happens past the 7-page cap.

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 four parameters are already documented in the schema. The description reinforces page-based pagination and the confirm requirement but adds no syntax or format detail beyond what the schema states, so baseline 3 is correct.

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+resource ('Retrieves paginated posts from a LinkedIn company page') and enumerates the returned fields (URL, ID, publication date, full text). This clearly distinguishes it from siblings like linkedin_company_page (page metadata) and linkedin_post (single post).

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?

Gives a prerequisite ('requires confirm=true') and a hard scope limit (max 7 pages), which shape how to call it. However, it never names an alternative or states when to pick this over linkedin_company_page or linkedin_post, so routing between siblings is left to inference.

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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