Scores a startup's monetization potential (0-21) from pricing anchors, model tags, and deal cycle, returning a label and pricing anchor detection.
Compute monetization_score (0-21) + label + has_pricing_anchors from pricing anchors + model tags + deal cycle hint.
| Name | Required | Description | Default |
|---|---|---|---|
| dealCycle | No | instant/days/weeks/months/quarters | |
| modelTags | No | e.g. ["subscription","usage","marketplace"] | |
| pricingAnchorsCount | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the behavioral disclosure burden. It does clarify that this is a compute operation with defined outputs, implying a pure calculation, but it does not state whether it is read-only, what 'label' means, how inputs map to the score, or whether external data is needed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with the output name and range front-loaded. Every phrase adds relevant information, and there is no redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description lists the main outputs but leaves 'label' undefined and does not explain how the deal cycle hint affects scoring. It is adequate for basic invocation but incomplete for a confident understanding of result semantics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 67%: dealCycle and modelTags have descriptions, but pricingAnchorsCount does not. The description maps 'pricing anchors', 'model tags', and 'deal cycle hint' to the parameters, but adds little beyond parameter names and does not explain the meaning of the deal cycle hint or the pricing anchor count behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific computation (monetization_score), states its output range (0-21) and additional outputs (label, has_pricing_anchors), and identifies the input categories. It is semantically distinct from sibling compute_* tools, though it does not explicitly contrast with them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No when-to-use guidance, prerequisites, or alternatives are provided. An agent must infer from the tool name that this is for monetization scoring, and there is no explicit routing away from other compute_* tools or mention of conditions that favor this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.