A
licenseNot graded
qualityC
maintenanceEnables AI assistants to analyze single-company concentration risk for tech workers with RSU compensation, providing risk scores, wealth-at-risk, and action items.
74
Apache 2.0
agentladle-mcp-reoi
by agentladle
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@agentladle-mcp-reoiRun REOI valuation for AAPL: sales 1000, WACC 10%, terminal growth 3%"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AgentLadle MCP REOI
δΈζ | English
A Model Context Protocol (MCP) server for Residual Operating Income (REOI) Valuation, built with Python and FastMCP.
π Financial Data & Valuation Engine β A professional quantitative analysis tool for Residual Operating Income modeling.
It enables AI assistants (like Claude, Cursor, etc.) to perform multi-stage residual income projections, discounting, and enterprise value bridging analysis via standardized data input interfaces.
Features
1 Professional MCP Tool providing comprehensive financial valuation capabilities.
Standardized REOI Framework, incorporating base period analysis, forecast period discounting, and terminal value estimation.
Multi-stage Profit Forecasting, allowing independent revenue growth rates and operating margins configuration for each year.
Smart Markdown Formatting, returning not only precise valuation figures but also built-in markdown tables for elegant rendering inside LLM clients.
Zero Configuration Installation β Add one line to your MCP client without cloning or manual setup.
Pure Python, cross-platform (Windows / macOS / Linux).
Related MCP server: SSCMFI Bond Analytics MCP Server
Prerequisites
Python 3.10+ β Download Python
uv β Install uv
Tip: After installing uv, restart your terminal and MCP client (e.g., Claude Desktop) to ensure the
uvcommand is recognized.
Quick Start
Add the following to your MCP client configuration (Claude Desktop, Cursor, etc.):
{
"mcpServers": {
"agentladle-mcp-reoi": {
"command": "uvx",
"args": ["agentladle-mcp-reoi"]
}
}
}That's it. uvx automatically downloads the package and its dependencies from PyPI β no cloning, manual installation, or path configuration required.
Alternative: pip install
If you prefer managing the environment yourself:
pip install agentladle-mcp-reoiThen configure:
{
"mcpServers": {
"agentladle-mcp-reoi": {
"command": "agentladle-mcp-reoi"
}
}
}Alternative: Run from Source (Local Dev)
Clone the repository and run directly:
git clone https://github.com/agentladle/mcp-reoi.gitConfigure your MCP client:
{
"mcpServers": {
"agentladle-mcp-reoi": {
"command": "uv",
"args": ["run", "--directory", "/path/to/mcp-reoi", "agentladle-mcp-reoi"]
}
}
}Replace /path/to/mcp-reoi with the actual path to the cloned repository.
Tool List
# | Tool | Description |
1 |
| Residual Operating Income valuation model. Outputs value per share and detailed breakdown based on financial statements and assumptions. |
Tool 1: reoi_valuation_model
Calculates enterprise equity value and suggested value per share by taking base period financial data and future forecast assumptions.
Parameter List (request object)
Parameter | Type | Required | Description |
| string | Version, default "1.0" | |
| string | Stock ticker | |
| string | Company Name | |
| string | Currency, default "CNY" | |
| object | β | Base period financial data |
| object | β | Valuation parameters |
| object | Optional market consensus data | |
| object | Optional forecast assumptions |
baseData object
Parameter | Type | Required | Description |
| float | β | Total Assets (millions) |
| float | β | Financial Assets (millions) |
| float | β | Total Liabilities (millions) |
| float | β | Financial Liabilities (millions) |
| float | β | Preferred Stock Value (millions) |
| float | β | Minority Equity (millions) |
| float | β | Base Period Sales (millions), must be > 0 |
| float | Base Period Operating Profit (millions) | |
| float | Base Period Core Profit (millions) | |
| float | Base Period Sales Growth Rate | |
| float | Base Period Operating Margin | |
| float | β | Total Shares Outstanding (millions), must be > 0 |
parameters object
Parameter | Type | Required | Description |
| int | Number of Forecast Years (default: 5) | |
| float | β | Discount Rate/WACC, e.g., 0.10 for 10% |
| float | β | Terminal Growth Rate, e.g., 0.03 for 3% |
marketConsensus object (Optional)
Parameter | Type | Required | Description |
| float[] | Array of annual revenue consensus | |
| float[] | Array of annual EPS consensus |
assumptions object (Optional)
Parameter | Type | Required | Description |
| float[] | Array of annual revenue growth rates | |
| float[] | Array of annual operating margins |
Data Flow
Model Input (Financials & Assumptions)
β
βΌ
Input Validation
β
βββ 1. Derive Base Net Operating Assets (NOA) and Asset Turnover
β
βββ 2. Forecast Period Projection (Compute sales, OI, ending NOA, residual income)
β
βββ 3. Terminal Value Calculation (Compute terminal value and discount to present)
β
βββ 4. Value Bridging (Core operating value + Financial Assets - Liabilities - Minority Equity)
β
βΌ
Markdown Detailed Output (Value per share, Data Tables)Tech Stack
Component | Choice | Purpose |
MCP Framework |
| MCP server with stdio transport |
Data Validation |
| Strong typing and JSON Schema generation |
Build Tool |
| Project configuration and dependency management |
Testing |
| Unit testing for the core valuation engine |
License
MIT
Available Tools
1 toolreoi_valuation_modelB
Residual Operating Income Valuation Model (Strict Replica based on Ladleagent frontend). Accepts a full JSON state object for valuation calculation.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| request | Yes |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
B3.2/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'Strict Replica' but does not disclose whether the tool is read-only, modifies data, requires permissions, or any other behavioral traits. The agent is left to infer that it computes a valuation without mutation.
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?
The description is concise at two sentences and front-loaded with the model name. It conveys the core function without fluff, but could benefit from added structure or examples without sacrificing brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of the input schema (multiple nested objects, many required fields) and the existence of an output schema, the description is too minimal. It does not summarize inputs, outputs, or calculation logic, leaving significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With schema description coverage reportedly at 0%, the description must compensate. However, it only vaguely refers to a 'full JSON state object' without explaining the one parameter 'request' or its nested structure. The agent lacks guidance on what fields matter.
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 clearly identifies the tool as a Residual Operating Income Valuation Model and states it accepts a full JSON state object for valuation calculation. The name and description align perfectly, leaving no ambiguity about the tool's function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by stating it accepts a full JSON state object, but provides no guidance on when to use it, prerequisites, or when not to use it. Since there are no sibling tools, the lack of alternatives is less critical, but basic usage context is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
v0.1.0- First observed
reoi_valuation_model
TDQS
B3.4/5.0
Disambiguation5/5
With only one tool, there is no risk of confusion. The tool's purpose is clear and distinct.
Naming Consistency5/5
Only one tool exists, so consistency is trivially maintained. The name is clear and descriptive.
Tool Count3/5
A single tool for a valuation model feels thin. Typically, a valuation server would benefit from multiple tools for different operations (e.g., parameter input, analysis, comparison).
Completeness2/5
The tool provides only a single calculation endpoint. Missing are tools for data management, scenario comparison, or model configuration, leading to significant gaps for a full valuation workflow.
Maintenance
ActivityStale
ResponsivenessNo issues
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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