get_macro_snapshot

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

With no annotations, the description carries full responsibility. It discloses the data content (FRED series id, observation date, 1/3/12-month changes), the computation basis (last real print vs calendar date), and failure handling (unavailable list). This is exemplary 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 front-loaded with the core purpose, then provides essential details in a compact, structured way. Every sentence adds value: data content, computation nuance, failure handling, and parameter guidance. No redundant text.

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 tool has an output schema, so return values need not be explained. The description covers data provenance, change calculations, error resilience, and parameter usage. It is complete for the tool's complexity, with no obvious gaps.

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 0%, so the description must compensate. It explains the `series` parameter with an example (["treasury_10y", "curve_10y_2y"]) and states that omitting it returns all series. This adds meaningful semantics 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 it returns current macro conditions from FRED, listing specific categories (rates, curve, inflation, jobs, vol). This is a specific verb+resource (get + macro snapshot) and distinguishes itself from siblings like get_macro_series by being a composite snapshot with computed changes.

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?

It explicitly instructs to always read rates and inflation here, warning that memory is wrong by whole percentage points. This provides a strong when-to-use directive and a when-not-to-use (memory). It also explains the optional `series` parameter for subsetting, giving clear usage context.

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