lazymac-mcp

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'real-time pricing,' implying dynamic data, but doesn't cover critical aspects like rate limits, authentication needs, error handling, or response format. For a tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

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, efficient sentence that front-loads the core purpose without unnecessary words. Every element ('50+ LLM models,' 'real-time pricing per token') earns its place by conveying essential information concisely.

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?

Given no annotations and no output schema, the description is incomplete for a tool that likely returns complex pricing data. It lacks details on response structure, error conditions, or behavioral traits like data freshness. This makes it inadequate for an agent to use the tool effectively without additional context.

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?

The input schema has 100% description coverage, with one parameter documented as a 'Free-form params object.' The description adds no additional parameter details beyond what the schema provides, such as example queries or specific filtering options. Baseline 3 is appropriate since 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 provides pricing information for LLM models, specifying '50+ LLM models with real-time pricing per token.' It distinguishes itself from siblings like ai_cost_calculator by focusing on real-time pricing data rather than cost calculation. However, it doesn't explicitly mention the verb (e.g., 'retrieve' or 'fetch'), which prevents a perfect score.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention siblings like ai_cost_calculator or llm_router, nor does it specify use cases, prerequisites, or exclusions. The agent must infer usage from the purpose alone.

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