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Legal Fees Apportionment

Legal Fees Apportionment MCP for AI. Calculate exact, auditable fee splits for judgments.

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
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Legal Fees Apportionment Engine splits judicial awards and attorney fees across multiple parties with exact proportional math. This MCP handles shared condemnations, ensuring every cent is accounted for and ratios are correct.

It deterministically calculates complex fee deductions and distributions based on claim weights or percentages. Stop dealing with rounding errors that invalidate agreements; use this to calculate precise settlements.

What your AI can do

Apportion legal fees

Splits a total judicial award among several parties while accurately deducting specific attorney fees.

Split Awards Among Parties

Determines how a total judicial award must be proportionally split among multiple named parties.

Deduct Attorney Fees First

Calculates and deducts specified attorney fees from the overall award before distributing remaining funds.

Handle Weighted Claims

Applies varying claim weights for co-plaintiffs, ensuring distribution reflects their proportional involvement in the case.

Calculate Specific Fee Types

Computes specialized fee allocations, such as sucumbência (costs paid by a losing defendant).

Included with Plan

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AI Agent

Legal Fees Apportionment Engine: 1 Tool Available

This MCP lets your agent deterministically split a total judicial award among several parties while accurately deducting specific attorney fees.

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Start using Legal Fees Apportionment Engine on Vinkius

Apportion Legal Fees

Splits a total judicial award among several parties while accurately deducting specific attorney fees.

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Claude AI

Claude AI

1

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

2

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

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Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

3

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Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Tracking Settlement Funds After Litigation

Right now, settling a case and splitting the money is an accounting nightmare. You get stacks of court documents listing awards, along with separate legal memos detailing who gets paid how much for their time. Then you have to manually build massive spreadsheets that try to reconcile all those numbers—the initial award, minus the fees, split by co-plaintiff weight, and then adjusted again if a specific party owes sucumbência.

With this MCP, your agent handles the entire process in one go. You input the raw data points, and it delivers the final, perfectly calculated distribution breakdown. It gives you the exact math needed to close out a file without ever worrying about rounding errors.

Apportion Legal Fees Apportionment Engine

The manual steps that vanish are the hours spent cross-checking decimal points, adjusting formulas for every new co-plaintiff added to the roster, and recalculating fees when a single variable changes. You stop toggling between Excel tabs and legal text documents.

What you get is mathematical certainty. The MCP gives you one definitive output: the correct distribution, ready for filing or payment.

What your AI can actually do with this

When a lawsuit ends and the money needs splitting, it’s rarely simple division. You have judicial awards, co-plaintiff claims, and mandatory attorney fees—all of which need to be accounted for proportionally. This MCP handles those exact calculations. It performs weighted division with high-precision decimal output, guaranteeing that the final distribution reconciles perfectly.

Your agent can feed it raw settlement data or complex claim weights, and it returns a clean, auditable breakdown. Instead of relying on general scripting tools that introduce rounding errors, this engine is built specifically for deterministic financial apportionment. You connect this Legal Fees Apportionment Engine through Vinkius to any MCP-compatible client (like Cursor or Claude) and get precise results without the headache.

It’s the math you can trust with high stakes.

Built · Hosted · Managed by Vinkius Legal Fees Apportionment - Split Awards & Fees
Server ID 019e38b6-a4a8-7297-946f-515ff8456ec5
Vinkius Inspector
Compliance Grade A+
Score 100/100
Vinkius Inspector Badge — Score 100/100

Questions you might have

How does the Legal Fees Apportionment Engine handle co-plaintiff weights? +

It handles weighted claims by allowing you to specify a ratio (e.g., 3:2:1). It ensures that distribution is proportional, not merely equal.

Can the Legal Fees Apportionment Engine split funds if fees are deducted first? +

Yes, it calculates and deducts specified attorney fees before performing the final proportionate split of the remaining award amount.

What kind of math does the Legal Fees Apportionment Engine use? +

It uses deterministic weighted division with high-precision decimal output. It avoids the common rounding errors found in standard calculators or basic scripting.

Is the Legal Fees Apportionment Engine useful for general budgeting? +

No. This MCP is specialized for legal finance, handling complex claims weights and specific fee types like sucumbência, not general operational budgets.

What data structure does `apportion_legal_fees` require for inputs? +

The tool requires three primary structured inputs: the total judicial award amount, the total percentage of fees to deduct, and a list of parties along with their specific claim weights. This required format ensures the calculation is deterministic and accounts for every variable precisely.

How does the Legal Fees Apportionment Engine handle invalid or conflicting data? +

If inputs fail validation—for instance, if a party weight is zero or the total award is negative—the engine stops immediately. It returns a structured error message detailing the exact point of failure, preventing any inaccurate results from being generated.

Are there rate limits when calling `apportion_legal_fees`? +

Usage adheres to standard Vinkius Marketplace API rate limits. For high-volume tasks or continuous processing across many records, you should check your subscription tier for dedicated throughput guarantees.

What types of AI clients support the Legal Fees Apportionment Engine? +

The engine uses the Model Context Protocol (MCP), making it compatible with any MCP-enabled client. You can connect it through your agent, whether that's in VS Code, Claude, or another dedicated workflow tool.

Does it handle unequal weights? +

Yes. Each party can have a custom weight. The engine computes exact ratios based on the total sum of all weights, ensuring mathematically perfect proportional distribution.

How precise is the rounding? +

The engine calculates ratios to 6 decimal places internally and outputs currency amounts rounded to exactly 2 decimal places, matching court-standard precision.

Can I deduct fees before splitting? +

Absolutely. You specify the fee percentage and the engine automatically separates the fee amount from the net award before distributing among parties.

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