Fair Value MCP Server

io.github.simonmak-ascentv0.2.1Updated Oct 3, 2026

Valuation MCP: DCF/NAV/CCA, cost of capital, derivatives, credit risk, report review.

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Overview

AI-generated overview

Lets an assistant run professional financial valuations such as DCF, NAV and CCA, price derivatives, model credit risk, and review valuation reports.

What it does
It exposes native valuation, cost-of-capital, derivatives, credit-risk and report-review tools, and also delegates the startup-valuation and intangible-valuation tool families (R15). Capabilities include DCF, NAV and CCA valuations, Fama-French 5-Factor cost of equity, the KMV credit risk model, and derivatives pricing for options, swaps and convertible bonds (R6-R9). It can review and validate Excel models and analyse PDF, Word and image documents (R10, R11). A machine-readable method catalog is served at the valuation://methods resource (R28).
When to use it
Use it when an assistant needs to produce or check a valuation rather than just discuss one: running DCF, NAV or CCA models, estimating cost of capital or credit risk, pricing derivatives, or auditing an existing Excel valuation model against IFRS/IVS-style standards (R2, R5-R11, R30-R33). It suits analysts and finance teams working with listed-company tickers and local report files. It is not a general-purpose data or spreadsheet tool.
Requirements
Local Python 3.8+ runtime (R20). Install from PyPI as fair-value with the mcp extra, then run the fair-value-mcp console command, or run it without installing via uvx --from fair-value[mcp] fair-value-mcp (R13, R14). Dependencies include yfinance, pandas, numpy, scipy, QuantLib-Python, openpyxl, pdfplumber and python-docx (R21-R26). Network access is needed for market data. No authentication, environment variables or headers are declared.
Before you install
The server reads local files: report review and document analysis open Excel, PDF, Word and image files (R10, R11), and scan_directory walks a directory (R17), so point it only at folders you are willing to expose. It fetches market data through yfinance (R18, R21), which sends ticker symbols to a third-party data source. No credentials, payments or write actions are declared.

Installation

In SourceWeft

  1. Open Fair Value MCP Server in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

Fair Value

[PyPI] [CI] [License: MIT] [MCP Registry]

Professional financial valuation system for OpenCode with IFRS/IVS compliance.

mcp-name: io.github.simonmak-ascent/fair-value

Overview

This project provides comprehensive financial valuation capabilities including:

  • DCF, NAV, and CCA valuations
  • Fama-French 5-Factor cost of equity
  • KMV credit risk model
  • Derivatives pricing (Options, Swaps, CB)
  • Excel model review and validation
  • PDF/Word/Image document analysis

Installation

bash
pip install -r requirements.txt   # dev workflow (unchanged)# or, as a package:pip install .                     # base librarypip install ".[mcp]"              # + MCP server dependencies (A-004)

The console command fair-value-mcp runs the MCP server (stdio; add --http for Streamable HTTP).

MCP Server

bash
# run locally without installing (stdio)uvx --from "fair-value[mcp]" fair-value-mcp
# or install and runpip install "fair-value[mcp]"fair-value-mcp            # stdiofair-value-mcp --http     # Streamable HTTP

The server exposes the native valuation, cost-of-capital, derivatives, credit-risk, and report-review tools, and delegates the startup-valuation and intangible-valuation tool families, so it is a strict superset of both.

Adoption target: ≥ 100 PyPI downloads and ≥ 1 directory listing within 90 days of the first release (tracked via the PyPI stats API and the directory listing).

Quick Start

python
from valuation_engine import run_valuation, review_report, scan_directory
# Run DCF valuationresult = run_valuation('9988.HK', 'dcf')
# Review Excel modelreview = review_report('/path/to/model.xlsx')
# Scan for valuation filesfiles = scan_directory('/path/to/reports/')

Module Structure

src/├── constants.py           # Standards references├── fetch_data.py          # Data fetching (yfinance)├── valuation/             # DCF, NAV, CCA├── cost_of_capital/       # WACC, FF5, KMV├── credit_risk/           # ECL calculations├── derivatives/            # Options, Swaps, CB├── report_review/          # Excel, PDF, Word, Image analysis└── output/                # Report formatting

Requirements

  • Python 3.8+
  • yfinance
  • pandas, numpy, scipy
  • QuantLib-Python
  • openpyxl
  • pdfplumber
  • python-docx

Documentation

See SKILL.md for the capability spec; the machine-readable method catalog is served by the MCP server at the valuation://methods resource, and a docs site is configured via mkdocs.yml.

Standards Compliance

  • IVS 2025
  • IFRS 13 (Fair Value Measurement)
  • IAS 36 (Impairment)
  • HKFRS 9 (ECL)

License

Released under the MIT License.

Source: README.md at commit 1f9d1bf

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Version history

1
  1. v0.2.1LatestOct 3, 2026