startup-valuation

Pre-Revenue Core Methods

valuation_core

Read-onlyIdempotent

Computes early-stage startup valuations using Scorecard, Berkus, Risk-Factor, VC Method, or exit terminal value. Supply the method and its required parameters.

Instructions

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').
`defaults_applied`	No	Optional parameters that were not supplied, so their documented defaults were used.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv2.1.1Oct 2, 2026

addedOutput schema / properties / defaults_applied

Added value: +{
+  "description": "Optional parameters that were not supplied, so their documented defaults were used.",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}

First observedv0.1.0Sep 30, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), yet the description adds substantial behavioral context beyond them: pure arithmetic with no I/O or external calls, results rounded to 2 decimals, no auth or rate limits, and explicit error behavior on unknown method or missing required parameter.

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?

Front-loads the method inventory and usage rule, then the per-method parameter mapping, then routing, then output/unit conventions — a logical order with no filler sentences. It is a dense single block and slightly long for one paragraph, but every clause carries decision-relevant information.

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?

Despite 17 parameters and 7 enum methods, the description supplies the per-method parameter mapping, unit semantics, error cases, and result shape; the output schema covers return fields (value, method, inputs, assumptions, chapter, formula_number, steps) which the description points to without duplicating. An agent has everything needed to call any method correctly.

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 the baseline would be 3, but the description adds cross-parameter conditional logic the schema cannot express: which parameters each method requires (scorecard → average_valuation + weights + scores; vc_pre_money → post_money + investment, etc.) plus unit conventions (fractions like 0.10, weight/probability lists in [0,1] summing to 1, method-dependent optional parameters that should be omitted).

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 resource (pre-revenue valuation methods) and enumerates exactly which methods it covers: Scorecard, Berkus, Risk-Factor Summation, VC Method both variants, and terminal value. It explicitly distinguishes itself from the sibling emerging/advanced/comparables tools, so an agent can route without opening any 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 ('Use these first for early-stage startups') plus three named alternatives with the exact conditions that select them (SAFEs/tokens/ESG/network effects → valuation_emerging; options or bull/base/bear tables → valuation_advanced; public comps → valuation_comparables). Nothing 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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