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Reach MCP — LinkedIn for AI agents

scrape_post

Read-onlyIdempotent

Fetch likers and/or commenters for a LinkedIn post. Pass the full post URL or slug. Set liked=true and/or comments=true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
likedNoInclude likers.
commentsNoInclude commenters.
account_idYesReach id of the LinkedIn account to act on, from list_accounts.
post_id_or_urlYesLinkedIn post URL or slug.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsNo
post_idNo
total_likesNo
total_commentsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds that it fetches likers and/or commenters, which is a useful behavioral detail, but it does not disclose things like pagination, rate limits, or what happens when both flags are false. With annotations covering the core behavior, a 3 is appropriate.

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?

Two sentences with zero waste. The core action and the two key parameters are front-loaded, and the instruction to pass URL or slug is direct. Every sentence earns its place.

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?

The tool has an output schema, so return values need not be explained. The description covers the input format and the two modes. It does not mention what happens if both liked and comments are false, but the schema defaults make that inferable. For a read-only fetch tool with full schema coverage, this is nearly complete.

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 four parameters. The description adds the relationship between liked/comments and the fetch modes, but it does not add meaning beyond what the schema provides. Baseline 3 is correct 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?

The description states a specific verb ('Fetch') and resource ('likers and/or commenters for a LinkedIn post'), and distinguishes it from siblings like scrape_profile and scrape_search. It also clarifies the two modes (liked/comments), so an agent can tell it apart from other scrape tools without opening the schema.

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?

The description gives clear context on what to pass (full post URL or slug) and how to select output (liked=true and/or comments=true). It does not explicitly state when not to use it or name alternatives, but the sibling list and the specific resource make the usage context clear enough.

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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