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Startup Valuation MCP Server

Hardware & Unit Economics

valuation_hardware
Read-onlyIdempotent

Hardware and deep-tech valuation: TRL-risk-adjusted valuation, gross margin, and break-even volume. Method selects the metric. Use for hardware and deep tech with technology-readiness risk; for drug pipelines use valuation_biotech. Parameters apply per method: trl needs market_size + market_share + margin + multiple + trl_discount; gross_margin needs asp + variable_cost; break_even_volume needs fixed_costs + asp + variable_cost. Not for drug pipelines — for those use valuation_biotech. Only method is required; all other parameters are method-dependent, so supply those the selected method names and omit the rest (defaults apply where defined). Rate and decimal inputs are fractions (0.10 = 10%); probability and weight lists are in [0,1] and sum to 1. Returns value, method, inputs, assumptions, chapter, formula_number and calculation steps; pure arithmetic — no I/O and no external calls — rounded to 2 decimals, with no auth or rate limits. An unknown method, or a missing method-required parameter, returns an error instead of a value.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aspNoAverage selling price per unit, currency units.
marginNoProfit margin as a decimal.
methodYesFormula to apply. Options: trl = V = market × share × margin × multiple × (1 - TRL discount).; gross_margin = GM = (ASP - COGS) / ASP.; break_even_volume = Units = fixed costs / (ASP - variable cost).
multipleNoExit or market multiple applied to the metric.
fixed_costsNoFixed costs for the period, currency units.
market_sizeNoTotal addressable market, currency units.
market_shareNoTarget market share as a decimal in [0,1].
trl_discountNoTRL risk discount as a decimal (applied as 1 - discount).
variable_costNoVariable cost per unit, currency units.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoError message when the call fails.
stepsNoIntermediate steps for traceability.
valueYesComputed valuation or metric.
inputsNoEcho of the normalised inputs used.
methodNoFormula / method name that produced the result.
chapterNoSource textbook chapter.
assumptionsNoModelling assumptions applied.
formula_numberNoSource textbook formula number (e.g. '3.1').
defaults_appliedNoOptional parameters that were not supplied, so their documented defaults were used.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already establish read-only, idempotent, closed-world, non-destructive behavior, and the description adds substantial context beyond them: pure arithmetic with no I/O or external calls, results rounded to 2 decimals, no auth or rate limits, and error behavior when the method is unknown or a required parameter is missing. That is exactly the extra operational detail annotations cannot carry.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well front-loaded: domain, method selection, then parameter requirements, then caveats and return info. Slightly redundant in restating the valuation_biotech exclusion twice and in re-explaining the return payload despite an output schema, but every other sentence is load-bearing.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 9-parameter, single-required-param arithmetic tool with full schema coverage, annotations, and an output schema, the description covers method selection, parameter requirements per method, units, error behavior, and non-I/O nature. An agent has everything needed to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description goes further by mapping each method to the parameters it needs (trl → market_size/market_share/margin/multiple/trl_discount, etc.), which the schema does not express. It also clarifies units (fractions, [0,1] probability/weight ranges) beyond the per-field schema notes.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific domain (hardware/deep-tech valuation) and the three concrete metrics it computes (TRL-risk-adjusted valuation, gross margin, break-even volume). It explicitly distinguishes itself from the sibling valuation_biotech, so an agent can route without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit when-to-use ('hardware and deep tech with technology-readiness risk'), explicit when-not ('Not for drug pipelines'), and names the alternative (valuation_biotech) twice. Nothing about tool selection is left to inference.

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

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