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scrape_my_posts

Read-onlyIdempotent

Fetch likers and/or commenters for the account's own recent posts. since_hours limits to posts published in the last N hours (default 24). Use for engagement on the account's own posts (likers, commenters, comment URNs for reply_comment); for someone else's post use scrape_post.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
likedNoInclude the people who liked.
commentsNoInclude the people who commented, with their comment text.
max_postsNoMax posts to process (max 20).
account_idYesReach id of the LinkedIn account to act on, from list_accounts.
since_hoursNoOnly posts from the last N hours (max 720).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
postsNo
total_postsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds real context: the scope is limited to the account's own recent posts, the since_hours window defaults to 24, and comment URNs feed reply_comment. It stops short of rate-limit or auth notes, but against rich annotations this is solid added value.

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 sentences, front-loaded with the core action in the first clause, followed by the scoping rule and the routing guidance. No filler; every sentence earns its place.

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

Completeness5/5

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

An output schema exists, so return format needn't be described, and the annotations cover the safety profile. Combined with explicit sibling routing and the since_hours scope, an agent has everything needed to call this correctly.

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 the schema already documents all five parameters including account_id, liked, comments, max_posts and since_hours. The description only restates the since_hours window and its default, adding no syntax or format detail beyond the schema. Baseline 3 applies when the schema does the heavy lifting.

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 (Fetch) and resource (likers/commenters for the account's own recent posts), and explicitly distinguishes itself from the sibling scrape_post ('for someone else's post use scrape_post'). An agent can select this over scrape_post without opening either schema.

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

Usage Guidelines5/5

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

Names the exact use case (engagement on the account's own posts) and enumerates the outputs that qualify (likers, commenters, comment URNs for reply_comment), then names the alternative tool and the condition ('someone else's post') that selects it. Nothing 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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