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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful behavioral context beyond those: 'it performs one HTTPS read, has no destructive side effects, and does not change board scoring, payments, wallets, files, or account state.' It also notes that source_date 'replays a known slice,' which is a behavioral trait not captured by annotations. No contradiction with annotations.

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 well-structured: it starts with the core purpose, then parameter summaries, then behavior, then usage scenarios. Every sentence carries information—no fluff or filler. The length is justified by the tool's diagnostic nature and the need to explain parameters and usage. It is front-loaded with the most important purpose statement.

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 (4 optional parameters, output schema present, annotations provided, and a clear sibling relationship), the description covers all necessary aspects: what it does, parameters, behavior, and when to use it relative to siblings. The output schema handles return-value details, so the description doesn't need to. It also addresses safety and idempotency, making it complete for agent invocation.

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. The description adds semantic value: 'limit is 1-100 for bounded samples' clarifies the purpose, 'source_date replays a known YYYY-MM-DD slice' explains the replay behavior, 'issuer_type narrows the audit' describes the filtering role, and 'include_rows=true attaches full publishable audit rows and should be used only for explicit debugging' provides contextual guidance. This goes beyond the schema's basic field descriptions, though it doesn't fully elaborate on all parameters.

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 opens with a specific verb and resource: 'explain why the alpha-opportunity board includes, excludes, or demotes rows.' It clearly identifies the tool as a read-only diagnostic for the alpha-opportunity board and names the output summaries (issuer-type, identity, quality-gate, raw-alpha-versus-board-rank), distinguishing it from siblings like deltasignal_alpha_opportunities and deltasignal_alpha_sweep.

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 states when to use the tool: 'after deltasignal_alpha_opportunities or deltasignal_alpha_sweep when the user asks why a high raw alpha row is missing, why ETF/trust/fund rows are excluded by default, why a row was demoted, or whether a screen is safe to summarize.' It also provides an exclusion: 'include_rows=true ... should be used only for explicit debugging.' These are concrete, actionable guidelines.

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