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Reach MCP — LinkedIn for AI agents

like_post

Idempotent

Like a LinkedIn post on behalf of the connected account. Pass the post URL, slug, or URN.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
account_idYesReach id of the LinkedIn account to act on, from list_accounts.
idempotency_keyNoOptional. A key you choose (a UUID is fine) that names this exact call. If you retry with the same key and the same arguments, the first call's result is returned and nothing is done twice on LinkedIn. Reusing a key with different arguments is refused. Keys expire after 24 hours.
post_url_or_urnYesLinkedIn post URL, slug, or urn:li:activity:….

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
likedNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false, idempotentHint=true, and destructiveHint=false, which covers the core safety profile. The description adds the 'on behalf of connected account' context and input format, but does not disclose anything beyond what annotations and schema already imply (e.g., whether unlike is possible, rate limits).

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 two short sentences, each earning its place. The core action is front-loaded, and the input formats are specified in the very same sentence. No repetition or filler.

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?

For a simple action that mutates a post like state, the description combines with a 100% documented schema and an output schema to cover everything an agent needs. The idempotency and safety profile are already in annotations. Nothing important is left unexplained.

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% and includes the full meaning of both parameters. The description's mention of 'post URL, slug, or URN' restates what the schema already says for post_url_or_urn, so it adds no new semantic information.

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 ('Like'), resource ('LinkedIn post'), and scope ('on behalf of the connected account'), which clearly separates it from sibling tools like comment_post or send_message. It also covers the three accepted identifier formats (URL, slug, URN), leaving no ambiguity about what the tool targets.

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 makes it obvious when to use this tool — whenever the user wants to like a post. It gives concrete input guidance ('Pass the post URL, slug, or URN') but does not explicitly contrast with alternatives or state when not to use it, so it stops short of full sibling 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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