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

Annotations already handle readOnlyHint and destructiveHint, but the description adds useful behavioral context: the call fans out to 80+ sources in parallel and returns 'categorized data blocks with source attribution and metadata.' It clarifies that the tool is an aggregator of third-party data sets, not merely a local read query. No contradiction exists with the read-only annotation.

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 substantial — five sentences — but each sentence earns its place: layer definition, call condition, source list, return format, and sequencing with search_graph. It is structured and readable, though slightly denser than necessary.

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?

Without an output schema, the description covers the essential expectation: 'Returns categorized data blocks with source attribution and metadata.' It also connects to search_graph's annotations and lists example sources. It does not spell out possible error cases or pagination, but for a read-only data-returning tool this is sufficient for first-line usage.

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 schema description coverage is 100%, so the baseline is 3; the description's main parameter guidance is implicit through scenarios like 'demand/attention-shaped queries.' It does not add parameter-level meaning beyond the schema, but because the schema is thorough, the definition still supports correct invocation.

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 purpose with concrete nouns: 'macro, institutional, and real-time market data' and enumerates source examples (Google Trends, FRED, BLS, Census). It also distinguishes the tool from the sibling search_graph by explicitly framing it as the call after search_graph reports thin/empty coverage.

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 explicit decision criteria: 'Use this tool when curated coverage is thin, empty, or when the query is explicitly demand/attention-shaped.' It also provides the fuller execution context — call after search_graph sends back a thin/empty coverage annotation — so an agent can determine when to invoke this tool over alternatives.

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