get_announcement_sentiment

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

No annotations are provided, so the description carries full burden. It details the output (per-announcement scores, confidence, hedging, topics) and company-level summary. It also explains the score range (-1 to +1). It does not disclose potential limitations like data freshness or authentication, but the core behavior is well-covered.

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 concise (three sentences) and front-loaded: first sentence states what it does, second lists outputs, third gives example queries. No redundant information.

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 complexity (6 parameters, no output schema), the description covers the main output categories and use cases. It could be more explicit about the data source (NZX announcements) and note that it only applies to NZX companies, but the ticker parameter description clarifies the scope.

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 context by explaining the output and linking parameters (e.g., ticker, hedging, sentiment) to the analysis, but does not elaborate on parameter semantics beyond what the schema already provides.

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 clearly specifies the tool returns AI-scored sentiment analysis of NZX company announcements, with concrete outputs like sentiment scores, hedging analysis, and guidance direction. It distinguishes itself from sibling tools (e.g., get_governance_scorecard, get_accounting_quality) by focusing on announcement sentiment.

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 explicit example queries ('sentiment on [company] announcements', 'buried risks') indicating when to use the tool. It does not explicitly state when not to use it or mention alternatives, but the sibling tools cover distinct domains, making usage clear.

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