Nanostores MCP

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

Annotations already indicate readOnlyHint=true and idempotentHint=true, so the agent knows this is a safe, repeatable read operation. The description adds valuable context beyond annotations by explaining that it returns stores 'ranked by activity' based on 'frequent changes, many action calls,' which clarifies the behavioral output and purpose, though it doesn't mention rate limits or authentication needs.

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 front-loaded with the core purpose and usage guidelines in the first sentence, followed by a concise example. Every sentence earns its place by providing essential information without redundancy, making it highly efficient 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 tool's complexity (diagnostic analysis), rich annotations (readOnlyHint, idempotentHint), and the presence of an output schema, the description is complete enough. It clearly explains the tool's role in performance investigation, distinguishes it from siblings, and provides usage examples, covering all necessary context without needing to detail return values (handled by output schema).

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%, so the schema fully documents the parameters (limit, windowMs, compact). The description provides example usage with {limit: 10} and {windowMs: 30000, compact: true}, which adds practical context but doesn't add semantic meaning beyond what the schema already specifies. This meets the baseline of 3 for high schema coverage.

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 with specific verbs ('find', 'returns', 'pinpoint') and resources ('stores ranked by activity'), explicitly mentioning 'frequent changes, many action calls' to distinguish it from sibling tools like nanostores_store_activity or nanostores_store_summary which might focus on different aspects of store behavior.

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 explicit guidance on when to use this tool: 'when investigating performance issues or excessive re-renders.' It also distinguishes it from alternatives by specifying it returns stores 'ranked by activity' to 'pinpoint bottlenecks,' helping differentiate from other sibling tools that might serve different diagnostic purposes.

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