Daily metric time series

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds crucial behavioral details beyond those: the multi-day lag, the meaning of missing values, and the int64-as-string serialization. This substantially improves agent understanding of response semantics.

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 yet information-dense, using two sentences to cover purpose, response shape, serialization, and lag. The structure is front-loaded with the core function, then details, and every clause carries value.

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 simple parameter set and no output schema, the description fully equips the agent: it explains the response structure, value type, missing-data semantics, and latency behavior. This is complete for the tool's complexity.

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 coverage is 100% and every parameter already has a clear description. The description adds no parameter-specific meaning beyond what the schema provides; it only repeats metric categories already captured by the enum. Baseline 3 is appropriate.

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 returns a daily time series for one performance metric, listing the specific metric types. The 'ONE' qualifier distinguishes it from the sibling fetch_multi_daily_metrics, making the purpose and scope unambiguous.

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 implies usage for single-metric queries but does not explicitly reference alternatives or when not to use the tool. It lacks the direct 'use X instead' guidance seen in high-scoring examples, so the guidance is only implied.

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