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 states the tool 'Returns pre/post event vol stats across multiple historical occurrences,' which implies a read-only, analytical operation without side effects. However, it lacks details on rate limits, authentication needs, data freshness, or error handling. For a tool with zero annotation coverage, this is insufficient to fully inform the agent about behavioral traits.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by bullet-like details for parameters and return value. Every sentence adds value without redundancy, and the structure is clear and efficient, making it easy for an agent to parse quickly.

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 complexity (historical event analysis), no annotations, no output schema, and low schema coverage, the description is moderately complete. It covers the purpose and parameter semantics well but lacks usage guidelines, detailed behavioral context, and output specifics. For a tool with 2 parameters and analytical focus, it's adequate but has clear gaps that could hinder optimal agent 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?

The input schema has 0% description coverage, so the description must compensate. It adds significant meaning: it explains that 'event' is an event name with examples (e.g., CPI, FOMC), and 'product' is a financial instrument with a list (e.g., ES, NQ). This clarifies the semantics beyond the schema's bare titles. However, it doesn't specify if 'product' is optional (the schema shows a default of 'ES'), so there's a minor gap.

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: 'Analyze historical volatility impact of a specific economic event.' It specifies the verb 'analyze' and the resource 'historical volatility impact,' and distinguishes it from siblings like get_calendar or get_forecast_today by focusing on event-based volatility analysis. However, it doesn't explicitly differentiate from all siblings (e.g., get_similar_days might also involve historical analysis), so it's not a perfect 5.

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 lists example events and products but doesn't explain scenarios where this analysis is preferred over other tools like get_forecast_today or get_similar_days. There's no mention of prerequisites, exclusions, or comparative use cases, leaving the agent to infer usage based on tool names alone.

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