analytics_dcf_valuation

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 mentions building a model with sensitivity analysis, but lacks details on what the tool actually does (e.g., creates a spreadsheet, generates a report, returns data), permissions required, potential side effects, or output format. This is inadequate for a tool with 9 parameters and no output schema.

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 single, efficient sentence with no wasted words, clearly front-loading the core action ('Build comprehensive DCF valuation model'). It is appropriately sized for the tool's complexity, earning a high score for conciseness.

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 (9 parameters, no output schema, no annotations), the description is insufficient. It doesn't explain what the tool outputs (e.g., a file, a calculation result), how sensitivity analysis is performed, or any behavioral traits. This leaves significant gaps for an AI agent to understand and use the tool effectively.

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?

The input schema has high description coverage (89%), documenting most parameters well (e.g., 'discountRate' as WACC). The description adds no additional parameter semantics beyond what the schema provides, but since coverage is high, the baseline score of 3 is appropriate as the schema does the heavy lifting.

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 verb ('Build') and resource ('comprehensive DCF valuation model with sensitivity analysis'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'analytics_scenario_comparison' or 'analytics_sensitivity_analysis', which might have overlapping functionality, so it doesn't reach the highest score.

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 no guidance on when to use this tool versus alternatives, such as 'analytics_monte_carlo_simulation' or 'calculate_npv', which are related financial modeling tools. There is no mention of prerequisites, context, or exclusions, leaving the agent with minimal usage direction.

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