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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses significant behaviors: it costs 1 api_sat per call, and proof is verified by debit_or_deny before cost is incurred. It also describes the content of the SVG (balance hero, metrics cards, health gauge, tranche table, tool usage breakdown). While it doesn't detail error cases or rate limits, the most important behavioral traits (cost and verification flow) are clearly communicated.

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 exceptionally concise: two sentences that front-load the core purpose, followed by a compact list of return contents and cost/proof details. Every sentence earns its place, and there is zero fluff or repetition. It serves as a model of efficient, informative tool documentation.

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?

Given the tool's complexity (generates an SVG, incurs a cost, requires proof verification), the description covers all critical aspects: what it produces, how it relates to the sibling data tool, the cost, and the verification step. The output schema is separate, so return-value details are already available. The description is complete for an agent to decide when and how to invoke this tool correctly.

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 input schema already thoroughly documents all three parameters (days, npub, dpop_token) with detailed descriptions. The tool description itself does not add parameter-level semantics beyond what the schema provides. Per the scoring guide, a baseline of 3 is appropriate when schema coverage is high and the description doesn't need to compensate.

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 starts with a specific verb and resource: 'Generate a visual SVG infographic of your account statement.' It clearly distinguishes from the sibling excalibur_account_statement by stating it returns the same data but rendered as an SVG graphic. This makes the tool's purpose immediately clear and differentiates it from the likely data-only variant.

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 explicitly references the sibling tool excalibur_account_statement, saying 'Returns the same data as account_statement, rendered as a dark-themed SVG graphic.' This implies when to use this tool (when a visual infographic is needed) versus the alternative (raw data). It also provides practical usage context by mentioning the cost and proof verification. However, it doesn't explicitly state exclusions or conditions when not to use the tool.

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