Sakila MCP Server

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. It states the tool performs analysis and allows checking efficiency, implying a read-only operation, but doesn't clarify if it requires specific permissions, what the output format looks like, whether it's paginated, or any rate limits. For an analytics tool with zero 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 concise with two sentences that directly state the tool's function and scope. It's front-loaded with the main purpose and avoids unnecessary details. However, it could be slightly more structured by explicitly mentioning it's a read operation or linking to sibling tools.

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 has no annotations, no output schema, and moderate complexity (analytics with two parameters), the description is incomplete. It doesn't explain what 'inventory turnover analysis' entails (e.g., metrics like turnover ratio, time periods), how results are returned, or any limitations. For an analytics tool, this leaves the agent guessing about output format and behavioral 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?

Schema description coverage is 100%, with both parameters ('store_id' and 'category') well-documented in the schema. The description adds no additional parameter information beyond what's in the schema, such as explaining how 'category' relates to inventory turnover or providing examples. 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 as '在庫回転率分析を行います' (performs inventory turnover analysis) and specifies it allows checking inventory efficiency per work/film. This is a specific verb+resource combination, though it doesn't explicitly differentiate from sibling tools like 'get_category_performance' or 'get_store_stats' which might overlap in inventory-related analytics.

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 sibling tools like 'get_category_performance' or 'get_store_stats' that might provide related metrics, nor does it specify prerequisites or exclusions for usage. The agent must infer context from the tool name alone.

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