generate_report

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

Annotations provide idempotentHint=true and readOnlyHint=false, so the description does not need to restate safety. It adds useful behavioral context beyond the schema: the renderer visually distinguishes AI-interpretation sections, always attaches a 'not advice' disclaimer, and the tool enforces a specific analyst voice. This enriches the agent's mental model of what invoking the tool produces.

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 front-loaded with the core purpose and then adds only high-value operational guidance. The length is justified by the need to communicate report content standards, AI-interpretation handling, and voice requirements; there is no filler or repetition of schema details.

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?

The description covers when to call, what it does, how to mark interpretive sections, and the required writing style. For a tool with no output schema, it could more explicitly state what the call returns (e.g., a rendered report or a report object), but the phrase 'assembles ... into the standard report envelope' implies the output sufficiently for an agent.

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 50%, so the description must add parameter meaning. It clarifies that sections carry 'summary, tables, citations, and confidence' and gives precise semantics for metadata.kind = 'ai_interpretation', which the schema leaves open as additionalProperties. It does not cover every undocumented field (e.g., generatedAt, companyName), but it adds real value beyond the schema on the most behaviorally important 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 states a specific verb ('Assembles'), a specific resource ('a structured research report from ResearchSections ... into the standard report envelope'), and explicitly differentiates it from research-gathering tools: 'this tool does no research of its own.' It is immediately distinguishable from fact-finding siblings like financial_statements or latest_news.

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 when-to-use guidance: 'Use after gathering facts with other tools.' It also rules out using it as a research tool, and explains exactly how to handle interpretive content by setting metadata.kind = 'ai_interpretation'. This is strong routing guidance relative to the many sibling tools.

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