dcf_valuation

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

Beyond the readOnly and idempotent hints, the description discloses that the tool never forecasts, guesses, or defaults assumptions; echoes all assumptions for auditability; reports structural issues like wacc <= terminalGrowthRate in an `issues` field; and outputs calculations rather than a verdict. This is exactly the kind of contextual behavior annotations cannot convey.

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 long but information-dense, with each sentence contributing either scope, prerequisites, issue handling, or output interpretation. It could be more scannable with structure, but there is no filler or tautology.

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 no output schema, the description covers most of the contract: explicit assumptions in, auditable output, fairValuePerShare, issues reporting on structural problems, and non-advisory framing. It references metadata.kind and generate_report, giving an agent enough context to use the result responsibly; only the full result shape and the optional companyName/sharesOutstanding semantics remain implicit.

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?

Despite 0% schema-description coverage at the top level, the description names most assumption inputs: revenue growth path, EBITDA margin path, D&A/capex/NWC percentages, tax rate, WACC, terminal growth rate, and net debt. It omits the optional companyName and sharesOutstanding parameters, so it does not fully compensate for the schema gap, but it adds substantial semantic meaning beyond the raw field names.

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 ('Runs') and a concrete operation: a discounted-cash-flow valuation from caller-supplied assumptions. It clearly distinguishes this from report-generation or advisory tools, though it does not explicitly differentiate among DCF siblings like multi_stage_dcf_valuation.

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 gives strong usage context: reason about assumptions from financial_statements, ratio_analysis, and sector context before calling, and pair the output with an ai_interpretation section rather than treating fairValuePerShare as advice. It does not explicitly state when not to use this versus multi-stage, SOTP, or comparables, but the computation-vs-verdict framing provides clear orientation.

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