lemon-squeezy-mcp

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

With no annotations provided, the description carries the full burden. It discloses that the tool returns risk scores (0-100) and risk levels with detailed factors, which is good behavioral context. However, it doesn't mention potential side effects, performance characteristics, or authentication requirements that would be helpful for a prediction tool.

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 efficiently structured in a single sentence that front-loads the core purpose and then specifies the return values. Every element earns its place with no redundant information 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?

For a prediction tool with no annotations and no output schema, the description does well by explaining what it predicts, what factors it analyzes, and what it returns. However, it could benefit from more detail about the prediction methodology or limitations given the complexity of churn risk analysis.

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 100%, so the schema already documents all three parameters thoroughly. The description doesn't add any additional meaning about the parameters beyond what's in the schema. Baseline 3 is appropriate when the schema does the heavy lifting.

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 clearly states the tool's purpose with specific verb ('predict') and resource ('churn risk for active subscriptions'), and distinguishes it from siblings like 'analyze_churn_risk' by focusing on prediction rather than analysis. It specifies the scope ('active subscriptions') and what factors are analyzed.

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?

The description implies usage for predicting churn risk based on specific factors, but does not explicitly state when to use this tool versus alternatives like 'analyze_churn_risk' or other customer/subscription tools. It provides context about what it does but lacks explicit guidance on when it's most appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.