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anmka-j

Subscription Sniper — AI Agent for Recurring Spend Audits

by anmka-j

check_engagement_signals

Analyze recent non-billing emails from a service to identify whether a subscription has high engagement or no engagement.

Instructions

Search recent non-billing mock emails from a sender to classify a subscription as HIGH_ENGAGEMENT or NO_ENGAGEMENT.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
service_nameYesSubscription service name to check for recent non-billing activity
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states a search and classification action but does not clarify whether the operation is read-only, destructive, or what side effects (if any) occur. The terms 'recent' and 'mock emails' are ambiguous, lacking details on time windows or the nature of such emails.

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 front-loads the action and outcome. Every word contributes meaning; there is no redundancy or irrelevant detail.

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 that there is no output schema, the description should clarify how the classification is returned (e.g., as a string, a boolean, or a side effect). It fails to define what constitutes 'recent' (e.g., emails within the last 30 days) and what 'mock emails' implies. These gaps make the description incomplete for an agent to reliably invoke the tool.

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 single parameter 'service_name' with a clear description. The tool description adds valuable context by specifying 'mock emails' and the binary classification result (HIGH_ENGAGEMENT or NO_ENGAGEMENT), which enriches the parameter's meaning beyond the schema alone.

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 action: search recent non-billing mock emails from a sender, and the outcome: classify subscription as HIGH_ENGAGEMENT or NO_ENGAGEMENT. This verb+resource combination is specific and distinct from sibling tools which handle financial metrics or cancellation playbooks.

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?

The description provides no explicit guidance on when to use this tool versus alternatives, such as when to check engagement before cancellation or financial analysis. It does not mention prerequisites, scenarios to avoid, or references to sibling tools.

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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