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

With no annotations provided, the description carries the behavioral transparency burden. It discloses non-obvious behavior: source attribution from world_bank/fred/ecb, GDELT as the global feed source, FinBERT sentiment inclusion, and an explicit 'structured real data only' promise with no directional signal. It does not describe response fields or error behavior, but for a read-style aggregation tool the stated constraints are meaningful.

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 dense and efficient, with no filler. It front-loads the core purpose, packs the output components and sources into one structured sentence, includes a compact region list, and ends with a crisp limitation statement. Every sentence earns its place.

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 is complex and has no output schema, yet the description covers the major output areas: macro indicators with source attribution, regional headlines, sentiment, and a global feed. It also supplies the region vocabulary and rejects narrative output. Exact response fields and error behavior are unspecified, but the description is sufficient for an agent to select the tool and understand what it will return.

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%, so the baseline is 3, but the description adds useful parameter semantics by enumerating the accepted region keys ('us, europe, japan, korea, china, hk, tw, sea, me, latam'), which the schema does not fully list. It also contextualizes sentiment and headline behavior in relation to the requested regions.

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 names a specific verb and resource ('Get a cross-region global market snapshot') and enumerates the exact contents: macro indicators, regional headlines, FinBERT sentiment, and a GDELT global feed. It also sets clear boundaries ('No AI narrative, no directional signal') that help distinguish it from sibling tools such as get_technical_analysis or get_sentiment.

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 intended use case is implied: call this when a cross-region, macro-plus-news-plus-sentiment snapshot is needed. However, it does not explicitly say when not to use it or name alternatives among the siblings, so an agent must infer the selection logic from the content description rather than being directly routed.

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