resolve_date_range

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does a strong job by explaining that the tool calculates on the server side using precise current time, enumerates supported expression types (including dynamic day ranges), and provides a full return format example. However, it doesn't discuss edge cases or failure behavior (e.g., invalid expressions, timezone handling), leaving minor gaps.

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 structured with a bold recommendation, rationale, workflow, Args, Returns, and Examples sections. It is front-loaded with the purpose and key information. However, it is somewhat verbose with examples that could be trimmed, though the examples add significant practical value for an AI agent, so the length is largely justified.

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 tool's complexity (parsing diverse natural language expressions) and low schema coverage, the description covers everything needed: supported formats, concrete examples, return structure, and integration with downstream tools. The output schema exists and the description's Returns section aligns with it, making the tool fully self-sufficient.

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?

The input schema only lists 'expression' as a string with no description, so the description is the only source of parameter explanation. It thoroughly enumerates supported expression formats (单日, 周, 月, 最近N天, dynamic) with examples in both Chinese and English, which fully compensates for the 0% schema coverage. The return format is also detailed, making the parameter semantics very clear.

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 resolves natural language date expressions into standardized date ranges. It distinguishes itself from sibling tools by being a date-resolution utility that other tools depend on for date_range parameters, and it explicitly positions it as the recommended first step in call flows.

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 provides extensive usage guidance: when to use it (whenever a user expresses dates in natural language), why it's needed (ensures consistent date calculations across AI models), and a recommended workflow with concrete steps and examples showing how to chain it with other tools like analyze_sentiment and search_news. It also shows what not to do implicitly by demonstrating the correct call sequence.

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