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

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 3, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the readOnly/idempotent/openWorld annotations, the description discloses that it is pure arithmetic with no I/O or external calls, rounds to 2 decimals, has no auth or rate limits, and returns an error (not a value) for an unknown method or missing required parameter. These are meaningful behavioral traits not derivable from the annotations.

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?

Dense but front-loaded: purpose, then method-to-parameter mapping, then sibling routing, then units and return behavior. Every sentence carries information, though the method/parameter list is long enough that it could be tightened slightly given the schema already documents fields.

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

Completeness4/5

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

For a 17-parameter, 7-method tool with an output schema, the description covers method selection, per-method parameter requirements, unit conventions, error behavior, and alternative tools. Return-field enumeration slightly duplicates the output schema, but overall nothing an agent needs to call it correctly is missing.

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 adds real value the schema does not: it maps each method to the parameters it consumes (e.g. 'scorecard needs average_valuation + weights + scores', 'berkus takes five factor awards') and clarifies fraction vs. [0,1] conventions. This is genuine semantic guidance beyond the per-field descriptions.

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 verb+resource ('The textbook's pre-revenue methods') and enumerates the exact methods covered (Scorecard, Berkus, Risk-Factor, VC Method, terminal value) plus the early-stage scope. This is clearly distinguishable from valuation_advanced, valuation_emerging, etc.

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 'use these first for early-stage startups' guidance and routes the agent away to three named siblings with the exact conditions ('for SAFEs, tokens, ESG... use valuation_emerging; for options or scenario tables use valuation_advanced; for public-comparable multiples use valuation_comparables'). When/when-not/alternatives are all present.

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

All Startup Valuation MCP Server tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.