calculate_dcf_valuation

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

No annotations are provided, so the description carries full burden. It details what the tool returns (intrinsic value, recommendation, projections, sensitivity analysis) and explains that inputs are customizable with defaults from historical data. It does not mention rate limits, destructive effects, or error states, but for a computational model, the transparency is adequate.

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 with sections, bullet points, and a table, making it readable. However, it is verbose: the 'What DCF Analysis Provides' section partially duplicates 'Returns'. The first sentence gets to the point, but the overall length could be reduced without losing clarity.

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 absence of an output schema, the description adequately covers return values (intrinsic value, recommendation, projections, sensitivity analysis) and input options. It explains the investment recommendation thresholds and sensitivity analysis scope. However, it could be more explicit about the output format (e.g., JSON structure) and edge cases.

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%, with each parameter having a clear description. The description adds a high-level overview of key inputs (e.g., 'Revenue growth rates', 'WACC') and groups them, but it largely paraphrases what is already in the schema. There is no extra semantic insight beyond the schema's own descriptions.

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 calculates intrinsic value using Discounted Cash Flow analysis, with specific focus on stock valuation. While the purpose is well-defined, it does not explicitly differentiate from sibling tools that might also compute valuations (e.g., 'get_analyst_ratings' or 'compare_peer_companies'). The title 'DCF Valuation | Intrinsic Value | Investment Analysis' reinforces the purpose.

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 lists concrete use cases (e.g., 'Calculate intrinsic value for Apple') and suggests when to enable sensitivity analysis. However, it provides no guidance on when NOT to use this tool or alternatives (e.g., for simple price checks, use get_quote). The usage context is implied but lacks exclusions.

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