trend

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

With no annotations provided, the description carries the full burden. It explains the tool's behavior: grouping by time period, computing counts, averages, or totals, and clarifies that omitting 'value_column' yields counts only. It does not mention auth needs, rate limits, or side effects, but the read-only nature is implied.

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 with a clear opening sentence followed by parameter explanations in a structured list-like format. It avoids redundancy and focuses on essential details, though the structure could be slightly improved with bullet points.

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 presence of an output schema (not shown), the description need not detail return values. It covers the core functionality and parameter semantics adequately. However, it could mention potential output format or restrictions (e.g., only numeric value_column) for fuller context.

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 each parameter: 'database', 'table', 'date_column', 'value_column' (optional with aggregation types), and 'period' (enumeration of groupings with default). This adds significant meaning beyond the bare schema.

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 analyzes time series data by grouping rows by time period and showing counts, averages, totals. It uses specific verbs and resources ('Analyze time series data', 'group rows by time period') and distinguishes itself from sibling tools like 'analyze' or 'compare' by its focus on temporal aggregation.

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 time-based data analysis but does not explicitly state when to use it versus alternatives like 'analyze' or 'compare'. No exclusions or when-not-to-use guidance is provided, leaving the agent to infer context from the tool name and parameters.

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