mcp-teslamate-fleet

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 that the tool 'shows weekly average efficiency from driving data,' implying it is a read-only operation that retrieves historical data. However, it lacks details on permissions, data sources, rate limits, or whether it requires authentication. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior and constraints.

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 concise and well-structured, consisting of two short sentences that efficiently convey the tool's purpose. The first sentence introduces the main concept ('Energy consumption trends — Wh/mi over time'), and the second provides specific details ('Shows weekly average efficiency from driving data'). There is no unnecessary information, making it easy to read and understand quickly.

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 tool's complexity is low (one parameter, no nested objects) and an output schema exists, the description is somewhat complete for basic understanding. It explains what the tool does but lacks details on parameter usage, behavioral traits, and differentiation from siblings. With no annotations and low schema coverage, the description should do more to compensate, but the presence of an output schema reduces the need to explain return values, keeping it at an adequate but minimal level.

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 one parameter ('days') with 0% description coverage, meaning the schema does not explain its purpose. The description does not mention this parameter at all, failing to compensate for the low schema coverage. However, since there is only one parameter and an output schema exists, the baseline is adjusted to 3, as the description does not add value beyond the schema but the simplicity of the tool mitigates the impact.

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: to show energy consumption trends measured in Wh/mi over time, specifically weekly average efficiency from driving data. It uses specific terms like 'energy consumption trends,' 'Wh/mi,' and 'weekly average efficiency,' which are more informative than just restating the name. However, it does not explicitly differentiate this tool from its sibling 'tesla_efficiency_by_temp,' which might handle efficiency data filtered by temperature, leaving some ambiguity in sibling differentiation.

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 does not mention any prerequisites, exclusions, or comparisons to sibling tools like 'tesla_efficiency_by_temp' or 'tesla_drives,' which might offer related data. Without such context, users must infer usage based on the tool name and description alone, which is insufficient for clear decision-making.

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