get_series

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

With no annotations, the description carries the full burden, and it delivers: it discloses inline return behavior, a 1000-row cap, context charging, bucket interval semantics, 1-based indexing, and even instructs the agent to report the topic name back to the user. This goes well beyond a basic operation description.

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 compact and well-structured: the headline result is first, followed by usage trade-offs, parameter formats, and a final agent-facing instruction. Every sentence adds value, and code spans keep it scannable.

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?

The description covers the tool's purpose, alternatives, parameter formats, result shape, and operational constraints, so an agent can invoke it correctly. The only notable gap is the undocumented 'car' parameter, which prevents it from being fully complete.

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 start/end as ISO-8601, bucket as a Postgres interval with examples, and index as 1-based. However, the 'car' parameter is not explained at all, and 'topic' is only indirectly referenced via the reporting instruction.

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 downsampled time series inline with avg, min, max, and count per bucket. It uses a specific verb and resource, and explicitly distinguishes itself from export_series by positioning this tool for reading numbers versus bulk/plotting 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?

It gives explicit when-to-use guidance: use when you need to read the numbers to answer a question, and use export_series instead for plotting or bulk analysis. It also provides bucket sizing advice to keep results manageable, which is actionable and context-rich.

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