get_pace_curves

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

With no annotations, the description carries the full burden. It explains what the tool does (analyzes pace data across running/swimming activities), how it works (finds best pace outputs for durations), and its return format (JSON string). It also discloses the GAP option. This goes beyond a simple statement, though it does not explicitly declare read-only behavior or potential limitations. The behavior is well-described for a query tool.

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 well-structured with a clear opening statement, a brief explanation, an Args block, and a Returns line. It is slightly longer than necessary but every sentence adds value, and the structure makes it easy to scan. The args block partially duplicates schema information but adds useful override/default details.

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 simplicity and the presence of an output schema (context signals indicate 'Has output schema: true'), the description does not need to detail return fields extensively. It covers the tool's purpose, parameters, and an example of use. The description is complete enough for an agent to select and invoke the tool correctly, though it could mention what the JSON output contains in more detail if no output schema were provided.

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 already covers 100% of parameters with descriptions, providing a baseline of 3. The description adds meaningful semantics beyond the schema: it notes that 'days_back overrides time_period' and clarifies the effective default of 90 days for time_period, which is not evident from the schema (default null). This added context improves understanding of parameter interactions.

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 pace curve data showing best efforts for various durations.' It provides specific examples of durations (400m, 1km, 5km, 10km) and mentions running/swimming activities, making it easy to distinguish from related tools like get_best_efforts or get_pace_histogram. The verb 'Get' and resource 'pace curve data' are precise.

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 includes 'Useful for tracking running fitness and race predictions,' giving clear context for when to use the tool. However, it does not explicitly mention alternatives or when not to use it, so it falls short of a 5. The usage context is clear but lacks exclusions or sibling comparisons.

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