Startup Valuation MCP Server

Pre-Revenue Core Methods

valuation_core

Read-onlyIdempotent

The textbook's pre-revenue methods: Scorecard, Berkus, Risk-Factor Summation, VC Method (post- and pre-money), and exit terminal value. Use these first for early-stage startups. Method selects the formula, and each method names its own parameters: scorecard needs average_valuation + weights + scores; berkus takes five factor awards; risk_factor needs base_valuation + risk_ratings; vc_post_money needs terminal_value + target_return; vc_pre_money needs post_money + investment; terminal_value needs projected_revenue + multiple; triangulated needs the scorecard inputs plus terminal_value/target_return/investment. Routing: for SAFEs, tokens, ESG, network effects, or data-moat methods use valuation_emerging; for options or bull/base/bear scenario tables use valuation_advanced; for public-comparable multiples use valuation_comparables. 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

Name	Required	Description
`method`	Yes	Formula to apply. Options: scorecard = V = V_avg · Σ(wᵢ·sᵢ) across 7 factors.; berkus = V = Σ factor awards, each capped at $500K.; risk_factor = V = V_base + Σ(rᵢ·$250K) over 12 risks.; vc_post_money = Post = Terminal / target ROI.; vc_pre_money = Pre = Post - Investment.; terminal_value = Terminal = projected revenue × multiple.; triangulated = Runs Scorecard and the VC Method together and returns their mean.
`scores`	No	Factor multipliers aligned with weights (1.0 = average, >1 above average).
`weights`	No	Portfolio or factor weights, each in [0,1] and summing to 1 (same order as the paired value list).
`multiple`	No	Exit or market multiple applied to the metric.
`prototype`	No	Berkus award for prototype / technology, 0 to 500,000.
`investment`	No	Amount invested, currency units.
`post_money`	No	Post-money valuation, currency units.
`sound_idea`	No	Berkus award for soundness of the idea, 0 to 500,000 (USD).
`quality_team`	No	Berkus award for management team, 0 to 500,000.
`risk_ratings`	No	12 risk factor ratings in [-2,2] (very low to very high); each unit shifts value ±250,000.
`target_return`	No	VC target return multiple (e.g. 10 for a 10x target).
`base_valuation`	No	Pre-adjustment baseline valuation, currency units.
`terminal_value`	No	Expected exit / terminal value, currency units.
`product_rollout`	No	Berkus award for product rollout / sales, 0 to 500,000.
`average_valuation`	No	Average pre-revenue valuation for the sector, currency units.
`projected_revenue`	No	Projected revenue at exit, currency units.
`strategic_relationships`	No	Berkus award for strategic relationships, 0 to 500,000.

Output Schema

TableJSON Schema

Name	Required	Description
`error`	No	Error message when the call fails.
`steps`	No	Intermediate steps for traceability.
`value`	Yes	Computed valuation or metric.
`inputs`	No	Echo of the normalised inputs used.
`method`	No	Formula / method name that produced the result.
`chapter`	No	Source textbook chapter.
`assumptions`	No	Modelling assumptions applied.
`formula_number`	No	Source textbook formula number (e.g. '3.1').

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 30, 2026

TDQS

A5/5.0

Behavior5/5

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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior. The description adds substantial context beyond annotations: pure arithmetic with no I/O or external calls, no auth, no rate limits, results rounded to 2 decimals, and an error return for unknown methods or missing method-required parameters.

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

Conciseness5/5

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

The description is long but front-loaded and dense: purpose and method list first, then method-to-parameter mapping, routing, and behavioral notes. Every sentence carries functional information, and the length is justified by seven methods and seventeen parameters.

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?

Given seven methods, seventeen parameters, an output schema, and rich annotations, the description covers purpose, sibling routing, parameter selection per method, defaults, return shape, and error behavior. Nothing essential for correct invocation is missing.

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

Parameters5/5

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

Schema coverage is 100%, so individual parameter meanings are documented. The description adds method-specific parameter requirements (e.g., scorecard needs average_valuation + weights + scores; vc_post_money needs terminal_value + target_return) and clarifies fraction/probability conventions, which are not in the schema and are necessary for correct invocation.

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?

Lists the seven named pre-revenue valuation methods it implements and positions them as the first choice for early-stage startups. It also names sibling routing targets, so an agent can distinguish it from valuation_emerging, valuation_advanced, and valuation_comparables without opening schemas.

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?

Explicitly says to use these methods first for early-stage startups and provides routing rules: SAFEs/tokens/ESG/network-effects/data-moat methods go to valuation_emerging, options/bull-base-bear tables go to valuation_advanced, and public-comparable multiples go to valuation_comparables. This gives clear when-to-use and when-to-use-else guidance, even if not every sibling is mentioned.

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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