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

Annotations already mark readOnlyHint=true and openWorldHint=true, and the description adds meaningful behavioral details: frequency aggregation 'cannot be finer than the native frequency', 'value is null where FRED reports .', the latest-N semantics of limit, and an API-key error hint. No contradictions with annotations.

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 long but every section earns its place: one-line purpose, parameter semantics, return shape, examples, and error guidance. It is clearly structured with labels, making it easy to scan, and the examples are high-value rather than filler.

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?

For a 7-parameter tool with one required parameter, this description fully equips an agent to invoke it correctly: it covers parameter combinations, return format, null handling, error behavior, and relationship to the FRED API key. Nothing necessary for correct invocation is missing.

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 coverage is 100%, but the description adds substantial value: frequency constraint versus native frequency, limit behavior in and out of a date range, unit transformation choices with examples, and realistic series IDs. The examples map entire user intents to parameter combinations, which goes far beyond the 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?

States a specific verb and resource: 'Fetch observations (time series data points) for a FRED series', with optional modifiers. It clearly distinguishes from series discovery by saying 'Don't use for series discovery — use search_fred_series first', and the examples reinforce the observation-fetching purpose.

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?

Explicitly tells the agent when not to use it ('Don't use for series discovery — use search_fred_series first') and gives four concrete natural-language-to-parameter examples. It also explains the behavior of limit with and without a date range, which is exactly the kind of usage nuance an agent needs.

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