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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds significant behavioral context: the presence of 'pending' estimates, 'last_verified' error stats, out-of-sample accuracy, backtest stats, consensus snapshot, and surprise calculation. It also differentiates between pro and demo scopes, which is not in annotations. This is valuable additional transparency beyond the structured data.

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 a single dense paragraph but is front-loaded with the core purpose and then elaborates on components. Every sentence adds information about data contents or scope behavior. It could be slightly more scannable with bullet points, but it is efficient and not redundant.

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 has no parameters and an output schema exists (per signals), the description need not explain return values. It adequately covers scope differences, data components, and the distinction between nowcast and consensus. It is complete for an agent to call correctly, though it could mention rate limits or data freshness, but that is minor.

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 tool has zero parameters, so the schema is trivially complete (100% coverage). Per the rubric, 0 parameters gives a baseline of 4. The description enhances this by detailing the output fields (consensus_bcf, actual_minus_consensus_bcf, est_bcf, etc.) and their meanings, adding semantic value beyond the empty 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 starts with a precise verb+resource: 'EIA weekly natural gas storage report (Bcf, lower-48 working gas)'. It clearly distinguishes itself from siblings by focusing on storage nowcast data, including actual, estimate, consensus, and surprise. The scope ('pro' vs 'demo') is explicitly defined, making the tool's purpose unambiguous.

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 provides clear context on when to use it: it covers both published actuals and pending estimates, with a note that 'demo scope returns only the already-published actual'. It also clarifies that `est_bcf` is a model nowcast, not the consensus, which helps avoid misuse. However, it does not explicitly name alternative tools or state conditions to prefer a sibling, so it loses one point.

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