Intangible Asset Valuation MCP Server

Royalty Rate Analysis

valuation_royalty_analysis

Read-onlyIdempotent

Royalty analysis: benchmark royalty-rate ranges by IP type and industry, adjust a base rate for deal factors, and apply the 25% rule of thumb. Method selects the formula. Use to select and support a royalty rate before a relief-from-royalty or royalty-capitalization valuation. To apply the chosen rate in a valuation use valuation_income_methods (relief_from_royalty) or valuation_market_approach (royalty_capitalization); for transfer-pricing pricing use valuation_compliance. Per method: benchmark needs ip_type + industry (optional: comparable_database); adjust needs base_royalty_rate + adjustment_factors; twenty_five_percent_rule needs licensee_expected_profit (optional: profit_attribution_to_ip). adjustment_factors multiply the base rate, so values above 1.0 raise the rate. Only method is required; all other parameters are method-dependent — supply those the selected method names and omit the rest (defaults apply where defined). Rates and premiums are decimals (0.10 = 10%). Pure arithmetic: no I/O and no external calls, rounded to 2 decimals; parameters belonging to other methods are accepted and ignored. 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: benchmark = Benchmark royalty-rate range by IP type and industry.; adjust = Adjusted rate = base rate * product of factors.; twenty_five_percent_rule = Royalty = licensee profit * IP attribution * 25%.
`ip_type`	No	Intellectual-property type, e.g. "patent", "trademark", "copyright", "trade_secret".
`industry`	No	Industry, e.g. "software", "pharmaceutical".
`base_royalty_rate`	No	Base royalty rate before adjustment, as a decimal (0.05 = 5%).
`adjustment_factors`	No	Multiplicative adjustment factors, e.g. {"profitability": 1.1, "market": 0.9}.
`comparable_database`	No	Optional comparable licenses, each {rate} or {royalty_rate}.
`licensee_expected_profit`	No	Licensee expected profit, in currency units.
`profit_attribution_to_ip`	No	Fraction of profit attributable to the IP, in [0,1].

Output Schema

TableJSON Schema

Name	Required	Description
`error`	No	Error message when the call fails.
`steps`	No	Intermediate calculation steps for traceability (one string per step).
`value`	Yes	Computed valuation, rate, or metric.
`method`	No	Formula / method name that produced the result.
`assumptions`	No	Modelling assumptions applied (list of strings or key/value object).
`defaults_applied`	No	Optional parameters that were not supplied, so their documented defaults were used.
`formula_reference`	No	Mathematical formula or reference applied.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 3, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already cover safety (readOnly, idempotent, closed-world), and the description adds substantive behavior beyond them: pure arithmetic with no I/O or external calls, results rounded to 2 decimals, defaults applied where defined, foreign-method parameters silently accepted and ignored, and errors returned for unknown methods or missing required params. This is exactly the disclosure an agent needs to invoke it safely.

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-loaded with purpose, then routing, then the method/parameter contract and units. Every sentence carries information, though the method-parameter enumeration makes it denser than strictly necessary and could be tightened.

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?

With an output schema present, the description need not explain return values; it covers everything else an agent needs — method selection, method-dependent parameters, unit conventions, error behavior, and the boundary against sibling valuation tools.

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%, yet the description still adds meaning the schema lacks: the per-method parameter mapping (benchmark needs ip_type+industry; adjust needs base_royalty_rate+adjustment_factors; twenty_five_percent_rule needs licensee_expected_profit), the direction of adjustment_factors ('values above 1.0 raise the rate'), and the decimal convention (0.10 = 10%).

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?

The description names a specific verb and resource ('benchmark royalty-rate ranges... adjust a base rate... apply the 25% rule') and explicitly distinguishes itself from siblings valuation_income_methods, valuation_market_approach, and valuation_compliance. An agent can identify its role as the rate-selection step preceding a valuation without reading any sibling definition.

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?

Explicit when-to-use ('Use to select and support a royalty rate before a relief-from-royalty or royalty-capitalization valuation') plus three named alternative tools for the downstream apply/transfer-pricing cases. The method-dependency rules further clarify which parameter set applies in each case.

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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