simulate_profit

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it describes the Ornstein-Uhlenbeck APY model components, mentions it runs 1,000 simulations, specifies the return format (optimistic/base/bearish scenarios with detailed metrics), and notes the PRO requirement and API key need. It doesn't mention rate limits or error handling, but covers core operational behavior.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by model details, simulation count, return format, and requirements. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.

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 (Monte Carlo simulation with multiple model factors), no annotations, and an output schema (which handles return values), the description is largely complete. It covers purpose, methodology, scale, output structure, and prerequisites. A minor gap is lack of explicit error cases or performance characteristics, but it provides sufficient context for effective use.

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. While it doesn't explicitly list parameters, it implies their semantics through context: 'pool_id' is referenced as 'specific pool', 'investment' and 'days' are implied by 'Project returns' and simulation duration, and 'compound' relates to return calculations. This adds meaningful context beyond the bare schema, though not exhaustive parameter details.

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's purpose with a specific verb ('Project returns') and resource ('for a specific pool'), distinguishing it from siblings like 'analyze_pool' or 'risk_score' by specifying Monte Carlo simulation methodology. It explicitly mentions what the tool does beyond basic analysis.

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 context for when to use this tool (projecting returns via simulation) and mentions 'PRO ONLY — requires PROFITSPOT_API_KEY' as a prerequisite. However, it doesn't explicitly state when not to use it or name specific alternatives among the sibling tools, though the simulation focus implies differentiation.

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