mcp-drizly

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 personalization based on preferences and order history, which hints at data usage, but lacks details on rate limits, authentication needs, response format, or whether it's a read-only operation. For a tool with 4 parameters and no annotations, this is a significant gap in transparency.

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. It directly states what the tool does, making it easy to parse. There's no redundancy or fluff, earning its place as a concise definition.

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 (4 parameters, personalization logic) and lack of annotations and output schema, the description is incomplete. It doesn't explain the return values, error conditions, or behavioral traits like data privacy implications. For a recommendation tool with no structured output, more context is needed to guide effective use.

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 description adds minimal meaning beyond the input schema, which has 100% coverage with clear parameter descriptions. It implies that 'preferences' map to categories and price ranges, and 'order history' maps to previousOrders, but doesn't elaborate on how these parameters interact or affect results. With high schema coverage, the baseline is 3, and the description doesn't significantly enhance parameter understanding.

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: 'Get personalized product recommendations based on preferences and order history.' It specifies the verb ('Get') and resource ('personalized product recommendations'), and distinguishes it from siblings like search_products or browse_categories by emphasizing personalization. However, it doesn't explicitly differentiate from all siblings (e.g., get_orders also involves order history).

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 when to prefer get_recommendations over search_products (which might be for broader queries) or get_orders (which retrieves order details rather than recommendations). There's no context about prerequisites, such as needing order history for effective personalization, or exclusions.

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