fred_data

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

With no annotations provided, the description carries the full behavioral burden. It does disclose the core query behavior and the key dual-input acceptance mode (registered name vs. arbitrary ID), and '查询' (query) implies a read-only operation. However, it says nothing about data freshness, caching, rate limits, or behavior on invalid series IDs, leaving those behaviors opaque.

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?

A single front-loaded sentence: purpose first, then the essential usage note, then high-value examples in parentheses. Every token earns its place and there is zero filler. For a tool with two optional parameters, this is appropriately sized.

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?

Because an output schema exists, the return shape need not be explained, which lowers the bar. What remains missing for fully confident invocation: the semantics of limit, the scope of data returned (observations over time?), and any pointer to fred_list for finding valid series IDs. The definition is functional but leaves these to agent inference.

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 meaningfully explains the series parameter by giving two accepted forms and concrete examples (fred_ppiaco, GDPC1, UNRATE), which goes beyond the bare schema. But it never explains limit (defaulted to 20), leaving an agent to guess whether it bounds observations, time periods, or something else.

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 states a specific verb and resource — 'FRED 数据查询' (FRED data query) — and reinforces it with concrete series_id examples (GDPC1, UNRATE). It clearly reads as the raw FRED series lookup tool among the sibling set, but it does not explicitly differentiate itself from domain-overlapping siblings like fred_list or us_economic_indicators.

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 gives operational guidance — pass either the registered name fred_ppiaco or any arbitrary series_id — which implies the main usage pattern. However, it never states when NOT to use this tool or points to alternatives (e.g., fred_list for discovering series IDs, macro_gdp for prepackaged series). The usage context is present but no exclusions or routing guidance.

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