Summary statistics for a series

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

Annotations already mark the tool as read-only. The description adds useful behavioral context: results are computed over the 'verified store,' a citation is attached, and the operation is cheap. These details go beyond the raw readOnlyHint and help set expectations about provenance and cost.

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 sentences, front-loaded with the output contents and followed by a concise usage rationale. No filler or repetition of schema details; every clause earns its place.

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?

There is no output schema, so the description carries the burden of explaining what is returned, and it does so by enumerating the statistics and citation. It leaves minor ambiguity about exact response structure but is complete enough for a simple, read-only stats 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 100%, with both parameters described and exemplified. The tool description does not add new meaning about source or seriesId, but it clarifies that these parameters identify the series whose statistics are returned. This is adequate but not additive.

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 names the exact resource (a series) and the specific statistics returned: latest, previous, min/max, mean, and comparisons. It also distinguishes itself from heavier analysis by billing itself as the 'cheap way' to answer a focused question, separating it clearly from deep_analysis.

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 frames when to use it: for quickly checking current value and movement 'without a full analysis run.' It does not name sibling alternatives like get_observations, but the use case and cost framing give an agent enough context to select this tool over a heavier analysis.

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