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DarkLvrd

agentic-linkedin

by DarkLvrd

Bootstrap a voice profile

bootstrap_voice_profile

Create an initial LinkedIn voice profile by analyzing past posts' emoji use and sentence length. Use it as a baseline for consistent, personalized communication.

Instructions

Derives a starting voice profile from the user's past posts (emoji use and sentence length); tune it afterwards.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
userIdYes
samplesYes
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses what inputs are analyzed (posts, emoji, sentence length) and that the result is a starting profile, but it does not say whether the tool persists anything, requires special permissions, or returns a profile object. Moderate transparency for an unannotated 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?

A single, front-loaded sentence with no wasted words. It states the action, the input source, the analysis dimensions, and the intended follow-up in one compact line.

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?

For a simple two-parameter tool with no output schema, the description provides enough to understand what the tool does and roughly how to call it. It lacks explicit return-value or persistence details, but these are partly implied by 'derives' and 'tune it afterwards.'

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 0%, so the description must compensate. It does add meaning by implying that samples are pieces of the user's past posts and that userId selects the user. However, it never explicitly maps each schema parameter or explains constraints like minimum sample count.

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 names an exact verb ('derives'), a specific resource ('starting voice profile'), and the method ('emoji use and sentence length'). It clearly distinguishes this bootstrapping step from sibling tools like get_voice_profile and set_voice_profile.

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?

'Tune it afterwards' clearly situates this tool as the initial step before further adjustment, which differentiates it from set_voice_profile. It gives context but does not explicitly name alternative tools or state exclusion cases such as 'do not use if a profile already exists.'

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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