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get_kol_influence

Calculate a KOL's influence score based on reach, engagement, and historical pick accuracy. Input the KOL's entity slug and select a 30 or 90 day window.

Instructions

KOL influence score: reach x engagement x historical pick accuracy. Pro tier. Param: entity_slug (the KOL's canonical entity slug). Supported windows: 30d, 90d.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entity_slugYesEntity slug.
windowNoRolling window. Supported windows: 30d, 90d.30d
Behavior2/5

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

No annotations are provided. The description only mentions a 'Pro tier' constraint but does not disclose any behavioral traits such as idempotency, side effects, error handling, or rate limits, which are critical for agent decision-making.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences covering purpose and key parameters. It is properly front-loaded with the purpose. A slight improvement would be to separate param details for clarity.

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?

There is no output schema, so the description should explain what the tool returns. It does not. Additionally, the window parameter discrepancy between description and schema reduces completeness. The tool lacks guidance on expected output format or 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 description adds context: 'entity_slug (the KOL's canonical entity slug)' and 'Supported windows: 30d, 90d.' However, the schema lists four enum values for window (1d, 7d, 30d, 90d), while the description only mentions two, creating a discrepancy that could confuse the agent. Schema coverage is 100%, so the description provides marginal added value.

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 what the tool computes: 'KOL influence score: reach x engagement x historical pick accuracy.' It names the specific resource (KOL) and the formula, distinguishing it from sibling tools that focus on other metrics.

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

Usage Guidelines3/5

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

The description mentions 'Pro tier,' implying usage restrictions, and lists supported windows. However, it does not explicitly state when to use this tool versus alternatives or when not to use it, leaving the agent without clear decision criteria.

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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