marketplace_revenue_forecast

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

With no annotations, the description carries the full burden. It discloses output characteristics (low/mid/high forecasts, confidence levels, trend analysis) and scope, which is useful. However, it does not disclose limitations (e.g., dependence on sufficient historical data), exact confidence level semantics, or any side effects. Some behavioral context is provided but notable gaps remain.

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 a tight three sentences: front-loads the main purpose, then output details, then scope. No filler or redundant phrases; every sentence adds meaningful information. Ideal length for the tool's complexity.

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 simple tool design (2 optional params, no output schema), the description is largely complete: it explains the core function, output categories, and scope. It could elaborate on confidence level interpretation and data prerequisites, but these are not critical for a basic forecast tool. Overall, it provides enough context for effective selection and invocation.

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% for both parameters, so the schema already documents days and toolId thoroughly. The description adds limited value: it repeats the toolId vs platform-wide distinction but does not mention the days parameter at all. Baseline 3 applies since the schema handles parameter semantics effectively.

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 specific language: 'Predict future revenue' with a clear resource (marketplace revenue) and method (historical data and statistical models). It distinguishes from sibling tools like marketplace_tool_revenue (actual revenue reporting) and predict_crypto_price (crypto price) by focusing on revenue forecasting. The scope (tool-specific vs platform-wide) is also clearly stated.

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 implies usage: use when you need future revenue projections for a tool or platform. However, it does not explicitly state when to use this over alternatives like marketplace_tool_revenue or marketplace_stats, nor does it mention exclusions or prerequisites. The usage context is clear but not well differentiated from siblings.

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