get_market_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 discloses output structure but not data freshness, limitations, or how sentiment is computed. It is a read-only summary, but additional behavioral context (e.g., data coverage range) would improve transparency.

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?

Two sentences: the first states the core purpose and filters, the second lists the returned information. No unnecessary words, front-loaded with the main action.

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?

Despite no output schema, the description enumerates the return structure. For a tool with 4 optional params and no required fields, the description is sufficient for invocation. It could add a usage example or explicit alternative, but overall it covers all key aspects.

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 0%, so the description must compensate. It explicitly mentions optional filters for sector/index/date, which maps to the 'sector' and 'index' parameters and 'to'/'from' as a date range. This adds meaning beyond the bare property names, though it doesn't detail formats or constraints.

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 uses a specific verb ('summarize') and resource ('market news & events dataset') and lists concrete outputs ('counts by sentiment and impact', 'top sectors', 'net bullish/bearish read'). This clearly distinguishes it from siblings like get_market_news, which likely returns raw 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 states optional filters by sector/index/date, implying use when a sentiment overview is needed. However, it does not explicitly contrast with get_market_news or other alternatives, so it provides clear context but no exclusions.

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