get_sentiment_forecasts

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

Annotations already declare readOnlyHint and idempotentHint. The description adds valuable behavioral context: it sources from a sentiment service, uses multiple models, and returns enabled:false if the service is disabled. No contradictions 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 concise but includes all necessary information: purpose, usage guidance, prerequisites, and return description. It is front-loaded with the key purpose. Could be slightly restructured for clarity, but not overly verbose.

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 has no output schema, the description adequately explains the return value: consensus forecasts including direction, probability, and confidence for multiple horizons. It also covers the service dependency and error condition (enabled:false), making it complete for a read operation.

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 100%, so the schema already documents both parameters (code and market). The description does not add any additional meaning or examples beyond what the schema 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 states it retrieves multi-model consensus forecasts for specific horizons (1d/5d/10d/1m/3m) with sentiment-driven direction, probability, and confidence. It uniquely identifies the tool as a future prediction tool, distinguishing it from sibling tools like get_stock_sentiment_v2.

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 lists example queries that should use this tool (e.g., 'next week's trend', 'future performance') and states priority over other tools. It also notes the prerequisite of backend sentiment service and the fallback response. However, it does not name alternatives or explicitly state when not to use.

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