lazymac-mcp

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 mentions estimating and comparing costs but fails to detail critical aspects like rate limits, authentication needs, response format, or whether it performs calculations locally or via API. This leaves significant gaps in understanding the tool's behavior.

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 functionality ('Estimate token cost') and elaborates with specifics ('across GPT-4 / Claude / Gemini / Haiku — compare 30+ models'). Every word contributes value without redundancy, making it highly concise and well-structured.

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 the complexity of cost estimation across multiple models, no annotations, no output schema, and a single but nested parameter, the description is incomplete. It lacks details on output format, error handling, or example usage, making it insufficient for an agent to fully understand how to invoke and interpret results.

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, documenting a single 'params' object as free-form parameters. The description adds no additional parameter semantics beyond implying model comparison, so it meets the baseline of 3 where the schema handles most of the documentation burden.

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: 'Estimate token cost across GPT-4 / Claude / Gemini / Haiku — compare 30+ models'. It specifies the action (estimate token cost) and scope (multiple AI models), though it doesn't explicitly differentiate from sibling tools like 'llm_pricing' or 'ai_spend_tracker', 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 lacks context such as prerequisites, comparison with sibling tools (e.g., 'ai_spend_tracker' for tracking vs. estimating, 'llm_pricing' for pricing details), or specific scenarios for estimation, leaving the agent with minimal direction.

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