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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. Beyond that, the description adds real value: the snapshot is isolated and read-only, results are 'regulatory-compliant,' and with no output schema it names the three returned visual components. It stops short of disclosing freshness, latency, or cost of the LLM path, which would have earned a 5.

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?

It is a single sentence, front-loaded with the verb and resource, with no filler or repetition. The parenthetical listing of three visual components is dense but earns its place given there is no output schema; the only mild cost is that the sentence is long and jargon-heavy for a first read.

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 three-parameter, fully schema-documented tool with no output schema, the description carries the right load by describing the returned artifacts rather than leaving them opaque. What is missing is comparative context — when an agent should pick this frame over get_macro_radar or get_market_telemetry — which is the main completeness gap.

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 100%, so the baseline is 3 and all three parameters — including the use_llm toggle and default_symbol fallback — are already documented in the schema. The description adds only the instrument-class enumeration (FX, metal, commodity, crypto, index), which lightly reinforces default_symbol but does not explain the tradeoff between LLM synthesis and the deterministic institutional summary.

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 gives a specific verb and resource — querying an isolated read-only market intelligence snapshot — plus the asset classes it covers and the shape of what comes back (analysis plus visualization frame). It falls short of full sibling differentiation: the 'macro causal transmission graph' overlaps conceptually with get_macro_radar, and the description never says how this frame differs from that tool or from get_market_telemetry.

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?

Usage is implied rather than stated: the asset-class list and the example query 'Where are retail traders trapped on Gold?' show the intended call pattern, but there is no explicit when-to-use, no when-not-to-use, and no routing advice across the four sibling tools. An agent must infer that this is the visualization-oriented choice versus the plain-data siblings.

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