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d3p_pricing-coach

Generate a complete pricing strategy with tier recommendations, psychological insights, and landing page copy based on your product, competitors, and target market.

Instructions

Generate a complete pricing strategy with tier recommendations, psychological insights, and landing page copy. Category: analysis | Cost: 10 sats | Endpoint: pricing-coach Parameters (pass as JSON string): main_competitor (string) (required): Your main competitor or similar product (optional, leave blank if none) product_description (string) (required): What your product does and its key features product_name (string) (required): The name of your product or service target_audience (string) (required): Who your customers are (e.g., 'startups', 'enterprise companies', 'individual creators') target_market_size (string) (required): How large is your addressable market (e.g., '10,000 potential customers')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsNo{}

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does describe the output (tier recommendations, psychological insights, copy) and mentions a cost of 10 sats, but does not disclose any side effects, rate limits, or error conditions. The behavioral transparency is adequate but lacks depth.

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

Conciseness3/5

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

The description is moderately concise but includes metadata (category, cost, endpoint) that may be redundant. The parameter list is detailed but could be more structured. The first sentence is clear and front-loaded.

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 an output schema (not shown) and a single-arg input schema, the description does not clarify how to invoke the tool (passing a JSON string vs. individual parameters) or what the return format is. It lacks completeness 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.

Parameters3/5

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

The input schema only has a single 'params' string with no description, while the description lists five separate parameters with explanations, adding significant meaning. However, there is a mismatch between the schema expecting a JSON string and the description listing parameters individually, which could confuse an AI agent. The description compensates partially.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it generates a complete pricing strategy with tier recommendations, psychological insights, and landing page copy. The verb 'Generate' is specific, and the resource is the pricing strategy. However, it does not differentiate from sibling tools that might also generate business advice.

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 does not provide any guidance on when to use this tool versus alternatives. It lists required parameters but no context on when the tool is appropriate or when to avoid it. No explicit when-not-to-use or alternative recommendations are given.

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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