get_signal_accuracy

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by explaining how signals are logged automatically when get_stock_brief or get_stock_debate runs, the 7-day price check, and the correct/wrong/neutral labeling. This adds meaningful context about the data pipeline and outcome determination. It could go further by disclosing data freshness or batch processing, but the provided information is solid.

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 well-structured and concise, with a clear lead sentence, a brief pipeline explanation, bullet-pointed use cases, and a compact Args/Returns section. Every sentence earns its place, and the format is easy to scan. No filler or redundancy.

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 tool is moderately complex with a data pipeline, and the description covers all needed context: data source, outcome labeling, use cases, parameters, and return summary (accuracy %, avg 7-day return, breakdown by signal type, top symbols). Since an output schema exists, the return-value summary is sufficient. The description is complete for an agent to select and invoke the tool correctly.

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 does so thoroughly with an Args section explaining each parameter: source (with possible values 'brief', 'debate', 'score', or '' for all), symbol (NSE symbol or all), and days (look-back window with default 30). This adds full meaning beyond the bare schema.

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 opens with a specific verb and resource: 'Show how accurate FinStack signals have been — backed by real outcome data.' It clearly distinguishes itself from sibling tools like get_signal_history (history of signals) and check_signal_outcomes (check specific outcomes) by focusing on aggregate accuracy statistics with a 7-day outcome window.

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 use cases ('Use this to: prove...', 'Find which signal source...', 'Find which stocks...') which give clear context for when to invoke this tool. However, it does not explicitly mention when not to use it or name alternative tools, so it lacks explicit exclusions. The guidance is still strong enough to steer an agent correctly.

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