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react_to_post

React to a LinkedIn post with a chosen reaction type (like, love, support, celebrate, insightful, funny). Provide the post URL and optionally a company URL to react on behalf of a company.

Instructions

Allows you to react to a post using any available reaction type (st.reactToPost action). If this workflow is still running, do not retry this tool; retrying can queue duplicate reaction attempts.

Linked API actions are queued into a cloud-browser workflow and may take several minutes. The server returns immediately after starting the workflow with {status: 'pending'|'running', pendingReason, workflowId, operationName, message}. To retrieve the final result, call get_workflow_result with the returned workflowId and operationName — it will long-poll until completion or the request budget elapses, then return either the final result or another in-progress snapshot. Do not retry the original tool while a workflow is still running; that creates duplicate queued work.

A pending workflow carries pendingReason: 'queued' means it is waiting its turn behind other work on the same account and will start within minutes. 'outsideWorkingHours' means the account has configured working hours and the workflow is parked until they reopen — possibly the next working day. In that case get_workflow_result returns immediately instead of polling, message states when the window opens, and you should report that to the user rather than looping.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEnum describing the reaction type.
postUrlYesLinkedIn URL of the post to react. (e.g., 'https://www.linkedin.com/posts/username_activity-id')
companyUrlNoLinkedIn company page URL. If specified, the reaction will be added on behalf of the company. (e.g., 'https://www.linkedin.com/company/acme-corp')
Behavior5/5

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

No annotations are provided, but the description thoroughly discloses the asynchronous workflow: queued actions, immediate server response with status fields, long-polling via get_workflow_result, and meanings of pendingReason values. This goes far beyond typical descriptions.

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 lengthy but every sentence contributes important behavioral context: async behavior, retry warnings, pending reasons, and result retrieval. It is well-structured with clear paragraphs and front-loads the purpose.

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?

Despite having no output schema, the description explains the returned status object, how to retrieve the final result via get_workflow_result, and how to handle different pendingReason scenarios. It fully covers the tool's complex async behavior.

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?

The input schema has 100% description coverage, so the schema already documents each parameter. The description adds no extra parameter-specific detail beyond what the schema provides, making the baseline 3 appropriate.

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 clearly states the tool's purpose with a specific verb ('react') and resource ('post'), and mentions it supports all reaction types. It distinguishes itself from siblings like react_to_comment and comment_on_post by focusing on reacting to a post.

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 provides clear guidance on when to use the tool and critical usage constraints, such as not retrying while the workflow is running and using get_workflow_result to poll for results. It doesn't explicitly name alternative tools for different actions, but the workflow context is well explained.

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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