analyze_series

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

With no annotations, the description must convey safety. It describes the operation as deterministic statistics but does not explicitly state that it is read-only or lacks side effects, leaving some ambiguity. It doesn't mention error behavior or permissions.

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?

Two concise sentences, front-loaded with the action, and no filler. The context about avoiding transcription errors is useful.

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?

Covers the main purpose and use case, and the output schema handles return-value documentation. It omits guidance on parameter interplay (e.g., window size vs. date range) but is reasonably complete for a computation tool.

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 only 33%, and the description only alludes to operations (growth rates, moving average, etc.) without explaining window, date boundaries, or geography. It adds value for operations but leaves other parameters underdocumented.

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 computes deterministic statistics (growth rates, moving average, rebasing, min/max/mean) on a single series, distinguishing it from siblings like get_series and compare_series.

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?

It explicitly advises using this over manual arithmetic when the series has many observations, providing a clear use case. It does not, however, specify when not to use it relative to comparative tools.

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