query_global_rates

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

With no annotations provided, the description carries the full burden. It discloses output to CSV, data sources (New York Fed, CBOE, etc.), fallback behavior (东财失败自动回退 Yahoo), and constraints (短端期限仅近 1000 交易日), providing rich behavioral transparency.

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 but dense, with each kind explained in a structured manner. It is slightly verbose due to the level of detail, but every sentence carries useful information, so it remains efficient.

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 all kinds, data sources, fallback logic, date ranges, and tenure constraints. It also mentions the output format (CSV). For a tool with this complexity, the description is complete and leaves no critical gaps for an agent to configure calls correctly.

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 description adds significant meaning beyond the schema. It explains each kind's data content, start dates, tenure options, and specific constraints (e.g., short-end tenures limited to ~1000 trading days). This goes well beyond the schema's basic field descriptions.

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's purpose: 'Query global rates for backtesting.' It specifies the resource (global rates) and the action (query), and the breakdown by kind (us_treasury, fed_rate, dxy, vix) distinguishes it from sibling tools like query_bond or query_fx.

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 backtesting and details data sources and constraints, but it does not explicitly contrast with alternative tools or state when not to use this tool. The guidance is implied rather than explicit.

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