Nanostores MCP

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

Annotations indicate read-only and idempotent operations, which the description does not contradict. The description adds valuable context beyond annotations by explaining caching behavior ('bypass cache' with force parameter) and output variations (compact vs. full modes), enhancing the agent's understanding of how the tool behaves in different scenarios.

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, followed by concise usage guidelines and examples. Every sentence adds value—no redundancy or filler—making it efficient for quick comprehension by an AI agent while maintaining clarity.

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 moderate complexity, rich annotations (readOnlyHint, idempotentHint), and the presence of an output schema, the description is complete. It covers purpose, usage scenarios, and parameter nuances without needing to detail return values, providing all necessary context for effective tool selection and invocation.

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?

With 67% schema description coverage, the description compensates by providing practical semantics: it explains when to use compact mode ('for a token-efficient directory-level overview') and force parameter ('to bypass cache'), adding meaning beyond the schema's basic descriptions. However, it does not address rootUri, leaving a minor gap in parameter context.

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 specific action ('Returns the complete store/subscriber/relation index') and resource ('for the project'), distinguishing it from siblings like nanostores_project_outline or nanostores_runtime_overview by focusing on indexing rather than outlining or runtime analysis. It explicitly mentions what is returned, making the purpose unambiguous.

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 compact mode ('for a token-efficient directory-level overview') versus full mode ('when you need to iterate over every entity or build a complete picture'), and includes an example for context. It clearly differentiates use cases, helping the agent choose appropriately without needing to infer from sibling tools.

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