Miyabi MCP Bundle

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

No annotations are provided, so the description carries full burden for behavioral disclosure. While 'calculate' implies a read-only operation, the description doesn't address important behavioral aspects like whether it handles empty arrays, large datasets, error conditions, or what format the results are returned in. It mentions metrics but doesn't explain what 'etc.' includes or behavioral 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 extremely concise - just one sentence with 8 words. It's front-loaded with the core purpose. However, the 'etc.' at the end is vague and doesn't earn its place, slightly reducing efficiency. Overall, it's appropriately sized for a simple calculation tool.

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 statistical calculation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns (format, structure), doesn't address edge cases (empty data, invalid metrics), and doesn't provide behavioral context. The 'etc.' is particularly problematic as it leaves the tool's capabilities ambiguous.

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%, with both parameters well-documented in the schema itself. The description mentions 'metrics' generically but doesn't add meaningful semantic context beyond what the schema already provides about the data array and metrics enum. The baseline score of 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: 'Calculate statistics: mean, median, stddev, variance, min, max, etc.' It specifies the verb ('calculate') and resource ('statistics') with examples of specific metrics. However, it doesn't explicitly differentiate from sibling tools like 'calc_expression' or 'calc_unit_convert', 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. There are multiple calculation-related sibling tools (calc_expression, calc_unit_convert), but the description offers no comparison or context about when this statistical calculation tool is appropriate versus those other calculation tools.

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