curistat-mcp

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the method ('feature-vector similarity'), scope ('Products: ES, NQ...'), and purpose ('probabilistic scenario planning'), but doesn't disclose rate limits, authentication needs, data freshness, or what constitutes 'similar' conditions. The behavioral context is adequate but incomplete.

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 perfectly concise with three focused sentences that each earn their place: establishing the core function, specifying products and method, and stating the practical application. No wasted words, front-loaded with the main purpose.

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 (similarity analysis across multiple products) with no annotations, 0% schema coverage, and no output schema, the description provides adequate high-level context but lacks crucial details about parameters, return format, similarity metrics, and behavioral constraints. It's minimally viable but has clear gaps.

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 0% schema description coverage and 2 parameters, the description provides no information about the 'product' or 'limit' parameters beyond what's in the schema. It lists product codes but doesn't explain their relationship to the 'product' parameter or what 'limit' controls. The description fails to compensate for the complete lack of schema documentation.

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 historical days', 'shows what happened next') and resources ('conditions similar to today', 'analog days'). It explicitly distinguishes from siblings by focusing on similarity-based historical analysis rather than forecasting, calendar events, or other market tools.

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 clear context for when to use this tool ('probabilistic scenario planning', 'useful for...'), but doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools. It implies usage for historical analog analysis rather than direct forecasting or event-based tools.

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