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

With no annotations, the description fully discloses the tool's behavior: it returns a calibrated YES/NO probability (or numeric/date range), rationale, news evidence, and optionally market analysis (model probability vs market). It also states it handles multiple question types. No hidden side effects are implied.

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 informative and well-structured, starting with when to call, then detailing output, an example, and supported question types. It is somewhat lengthy but front-loaded with the most critical information. A minor reduction in examples could improve conciseness.

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 (13 params, no annotations, but an output schema exists), the description covers the core use case and return format well. However, it leaves many parameters undocumented, which is a gap for an agent attempting to use the tool optimally. The output schema mitigates some completeness concerns.

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 explains only a few parameters (question, market_probability, market_url) and alludes to options/categories for multiple-choice, but 13 parameters exist (e.g., variant, description, question_type, resolution_criteria) with no explanation. The description does not add sufficient meaning beyond the schema for most 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 clearly states the tool is for forecasting questions about probability, likelihood, or whether something will happen, with explicit trigger examples and a detailed return specification (calibrated probability, rationale, evidence). The verb 'forecast' matches the resource, and the scope is well-defined, though it does not explicitly differentiate from siblings.

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 triggers (e.g., 'Will X happen?', 'What are the chances of Y?') and advises passing market_probability/market_url for market edge analysis. However, it does not explicitly state when NOT to use this tool or compare it to sibling tools like foresea_analyze_market or foresea_scan_markets, which would strengthen guidance.

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