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Publora MCP Server

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linkedin_create_reaction

React to a LinkedIn post by choosing a reaction type like LIKE, PRAISE, EMPATHY, INTEREST, APPRECIATION, or ENTERTAINMENT.

Instructions

React to a LinkedIn post (like, praise, etc.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
postedIdYesLinkedIn post URN, e.g. 'urn:li:share:123456'
platformIdYesPlatform connection ID
reactionTypeYesReaction type
Behavior2/5

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It only states the basic action without addressing idempotency, authentication requirements, side effects (e.g., replacing an existing reaction), or return value. This is insufficient for a mutation tool.

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 a single, well-structured sentence that conveys the core action with examples. There is no redundancy or superfluous text.

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

Completeness2/5

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

Given the tool has 3 required parameters and no output schema or annotations, the description lacks critical information for an agent: what the response looks like, error conditions, whether the reaction replaces or adds to existing ones, and required permissions. It is too sparse for reliable invocation.

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 coverage is 100%, so the description adds minimal value. The examples 'like, praise' hint at the reactionType enum values, but the schema already lists all options. No additional context is given for postedId or platformId beyond their descriptions in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'React' and the resource 'LinkedIn post', and provides examples ('like, praise, etc.') that align with the reactionType enum. It distinguishes this tool from siblings like create_post and linkedin_create_comment, but could be more precise about creating a reaction rather than just reacting.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives such as linkedin_create_comment or linkedin_delete_reaction. The description does not specify use cases, prerequisites, or when to avoid this tool.

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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