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Set Linkedin Warmup

set_linkedin_warmup

disabled=True opts out of the ramp (full daily volume right away — only safe on an already-warm account); disabled=False re-enables it (resumes any active ramp). Owner-only. Returns {success, warmup_disabled, is_ramping_up}, the recomputed ramp state. Dict with success, warmup_disabled, and is_ramping_up.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
disabledYesTrue to skip warmup (full volume now), False to re-enable the ramp.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations only declare non-readonly and non-destructive, so the description carries the behavioral burden. It adds meaningful context: it makes a real setting change, has an owner-only restriction, implies a safety condition, and returns the recomputed ramp state. It stops short of describing detailed effects on an active ramp or any side effects, but for a simple boolean toggle this is adequately transparent.

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 compact, front-loads the core purpose, and each sentence earns its place: what it does, when to call it, parameter behavior, and return value. It uses clear short phrases and structured summary/returns tags that are easy to scan.

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 one-parameter, no-output-schema tool, the description is complete: it covers purpose, trigger conditions, parameter semantics, safety caveat, ownership, and return keys. An agent has everything needed to call this tool correctly without external context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already describes the boolean parameter at 100% coverage, giving a baseline of 3. The description adds value by elaborating what True and False concretely do ('full daily volume right away,' 'resumes any active ramp') and by adding the safety condition. This exceeds the schema's plain wording without being redundant.

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 ('Turn ... off or on') and resource ('new-account warmup ramp for the user's LinkedIn account'), making the tool's function unmistakable. It also distinguishes this from merely describing the UI ('actually make the change, don't just describe the UI'), which separates it from sibling tools like set_linkedin_daily_limits.

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?

The description explicitly tells the agent when to call the tool: when the user asks to skip/disable warmup or re-enable it. It also flags 'Owner-only' as a prerequisite and warns that disabling the ramp is 'only safe on an already-warm account,' which is actionable guidance.

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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