benchmark_track

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

With no annotations provided, the description carries full burden. It mentions SPC-style tracking and actions, but does not disclose important behaviors such as mutability (does it overwrite existing data?), idempotency, persistence, or required permissions. The behavioral description is too sparse for a tool that likely modifies and queries stored data.

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 concise, with a clear trigger, title, and parameter list. Every sentence adds value, and it is front-loaded with the trigger condition. However, the TRIGGER line is slightly informal, and the structure could be more formal for consistency.

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 has 5 parameters and an output schema (but not detailed), the description covers all parameters and main use cases. However, it lacks explanation of what the trend action returns (e.g., chart or data points) and does not elaborate on the SPC concept, which is central to the tool's purpose. This leaves gaps for an AI agent unfamiliar with SPC.

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 0%, so the description must compensate. It explains each parameter: metric (with examples), value (required for record), unit, action (with intended use for each value), and context. This adds significant meaning beyond the schema. However, it lacks details on value constraints and unit format, so not exhaustive.

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 it is a benchmark tracker for performance improvements, using SPC-style baseline and delta tracking. It identifies the core function of recording and viewing benchmarks. However, it does not explicitly distinguish itself from sibling tools like research_benchmark_catalog, so it lacks differentiation.

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 a trigger condition ('Call this whenever making performance improvements') and lists actions (record, trend, list). This provides some usage context. However, it does not specify when not to use the tool or mention alternative tools for different scenarios, so guidance is incomplete.

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