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

With no annotations, the description bears full transparency burden. It discloses the methodology (logs forecasts before outcome, grades against reality), the public nature, and the specific metrics returned. This goes well beyond a simple 'get' but doesn't address potential permissions or side effects; however, the tool appears inherently read-only.

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 three sentences, front-loaded with the core concept, followed by a concise list of returned data, and ends with practical usage guidance. Every sentence earns its place with no redundancy or filler.

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 no output schema, the description adequately enumerates the key return fields (hit rate, band coverage, MAE, calibration scale, recent rows). It also explains the timing and public nature. While it doesn't detail formatting or edge cases, it is complete enough for an agent to select and invoke the tool correctly.

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 100%, with each parameter documented including defaults. The description adds context about outputs but no additional parameter-level meaning. This meets the baseline of 3 for well-covered schemas.

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: accessing a public forecast track record. It specifies the resource ('public forecast track record') and the action (returns quality metrics), distinguishing it from siblings like backtest_strategy or get_trading_decision by focusing on calibration verification.

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 explicitly says when to use it: 'Use this to verify the engine's calibration before trusting its forecasts.' This provides clear context, though it stops short of naming alternatives or explicit when-not-to-use scenarios, which would merit a 5.

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