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Valuation Skills MCP Server

Valuation Skills MCP Server

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github.comFinance1Updated 1d ago

About Valuation Skills MCP Server

A financial valuation system for OpenCode and AI agents, also available through a REST API and Python library. It covers corporate and intangible valuation, derivatives, credit risk, and actuarial methods, with results tied to IVS and IFRS/IAS clauses.

Key features

  1. 01DCF, NAV, CCA, and residual valuation
  2. 02WACC and market multiples
  3. 03Derivatives, credit risk, and fixed income
  4. 04Actuarial and structured-product valuation
  5. 05Results with statistics and standards citations

Use cases

  1. 01Calculating corporate or startup valuations
  2. 02Assessing credit risk and fair-value adjustments
  3. 03Reviewing valuation results against IVS and IFRS/IAS

MCP support

The MCP server exposes 14 `calculate_*` tools spanning 135 valuation methods. It supports stdio and Streamable HTTP, with machine-readable catalogs for methods and standards.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"simonmak-ascent-valuation-skills": {
"command": "uvx",
"args": [
"valuation-skills"
]
}
}
}
Run commanduvx valuation-skills

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypivaluation-skills0.1.1

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Fair Value

PyPI CI License: MIT MCP Registry TDQS

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

mcp-name: com.ascent-partners/fair-value

Overview

A single valuation core — exposed as an MCP server, a REST API, and a Python library — whose 14 native calculate_* tools / 135 methods cover corporate, startup, and intangible valuation aligned to IVS 2025 and IFRS/IAS:

  • DCF / NAV / CCA / residual income, market multiples, cost of capital (WACC, Fama-French 5-Factor, build-up)
  • Options, fixed income (term-structure curves), convertible bonds, structured products
  • IFRS 9 / HKFRS 9 credit risk (ECL, PD/LGD, CVA/DVA), actuarial PV (IFRS 17, IAS 19, IAS 37)
  • Fair-value adjustments (IFRS 13), expected value, and loss-making-company methods

Every result is a deterministic-first envelope carrying the centre value, solution_type, statistics (σ / percentiles, and seeded samples for the few stochastic methods), and citations resolving to verbatim IVS/IFRS clauses. 120 / 135 methods carry standards citations and the taxonomy has 0 orphan clauses, enforced by a conformance gate in CI.

Quick start (≤ 5 minutes)

Fastest path — no install:

  • Hosted MCP (Streamable HTTP): https://fair-value.ascent-partners.com/mcp
  • Hosted REST: https://fair-value.ascent-partners.com/v1/health
  • Frontage & docs: https://fair-value.ascent-partners.com/
  • Run locally, no install: uvx --from "fair-value[mcp]" fair-value-mcp

Then call any of the 14 calculate_* tools (add -help / help=true for the generated transparency record). Client config: MCP Server.

Architecture

flowchart LR
  CLIENT["AI agent / OpenCode"] -->|"stdio (uvx) or Streamable HTTP"| MCP["fair-value-mcp<br/>14 calculate_* tools · 135 methods"]
  HOST["Hosted · fair-value.ascent-partners.com<br/>docs/landing at / · MCP at /mcp · REST at /v1"] --> CLIENT
  MCP --> REG["method-spec registry<br/>(valuation://methods)"]
  REG --> STD["standards taxonomy + corpus<br/>IVS 2025 · IFRS/IAS · (valuation://standards)"]
  REG --> CORE["valuation engine<br/>DCF · NAV · CCA · WACC (FF5) · derivatives<br/>credit risk · actuarial"]
  CORE --> DATA["inputs from apdb-etl (cited)"]
  CORE --> ENV["result envelope<br/>value + statistics + citations"]

Installation

pip install -r requirements.txt   # dev workflow
# or, as a package:
pip install .                     # base library
pip install ".[mcp]"              # + MCP server dependencies (fastmcp)

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

MCP Server

# run locally without installing (stdio)
uvx --from "fair-value[mcp]" fair-value-mcp

# or install and run
pip install "fair-value[mcp]"
fair-value-mcp            # stdio
fair-value-mcp --http     # Streamable HTTP

The server exposes 14 native calculate_* tools spanning DCF, cost of capital, market multiples, residual/asset valuation, options, expected value, credit risk, actuarial PV, sector metrics, fair-value adjustments, convertible bonds, structured products, loss-making companies, and fixed income — all derived from one method-spec registry. Add -help to any tool (or help=true) for its generated documentation with formula reference, inputs, and governing clauses.

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).

REST API

The same core is exposed at /v1 (served next to MCP by mcp_server.asgi:app):

curl -s localhost:8000/v1/health
curl -s -X POST localhost:8000/v1/calculate/calculate_dcf \
  -H 'content-type: application/json' \
  -d '{"method":"dcf","cash_flows":[100,110],"discount_rate":0.1}'

Endpoints: /v1/health, /v1/tools, /v1/methods, /v1/standards, /v1/openapi.json, /v1/docs (Swagger UI), /v1/help/{tool}, POST /v1/calculate/{tool}. See REST API.

Web frontage (Next.js)

The hosted domain serves a Next.js frontage — landing, complete docs, methods catalogue (one page per method), standards browser, and an API playground — alongside the MCP/REST function in one Vercel project. It is data-driven: the catalogue is generated from the registry at build time (scripts/gen_web_data.py → data/catalog.json).

pnpm install
pnpm dev          # http://localhost:3000  (expects /v1 from the API for the playground)
pnpm build        # prebuild regenerates data/catalog.json
pnpm typecheck && pnpm lint

Routing: / and /docs/* are Next.js; /mcp and /v1/* are rewritten to the Python function (api/index.py). The frontage is localized — en at /, zh at /zh, hk at /hk — via per-locale root layouts with hreflang.

Quick Start (Python)

from valuation_engine import run_valuation

result = run_valuation('9988.HK', 'dcf')  # DCF valuation (shared envelope)

Standards & citations

The standards alignment is data, not code: standards/taxonomy.json maps each method to its IVS and IFRS/IAS clauses, standards/source/*.md holds the verbatim clause text (with standards/provenance.json recording edition and source), and standards/coverage-baseline.json is the coverage ratchet. The engine attaches solution_type and citations to every envelope, and scripts/check_conformance.py fails CI if a published method loses its citation or the taxonomy/corpus drifts. See Standards reference.

Module structure

flowchart LR
  SEED["method_spec_seed.py<br/>method tables · 135 methods"] --> SPEC["method_spec.py<br/>parameter vocabulary"]
  SPEC --> SURF["tool_surface.py<br/>15 calculate_* tools"]
  SPEC --> STD["standards.py<br/>taxonomy · citations"]
  SURF --> SRV["server.py (FastMCP)"]
  SRV --> STDIO["stdio · uvx fair-value-mcp"]
  SRV --> HTTP["Streamable HTTP · fair-value-mcp --http"]
  ASGI["asgi.py (+ rest.py, openapi.py)"] --> HOST["Hosted · / + /v1"]
  SRV --> DOCS["docs.py<br/>-help transparency records"]
  STD -.->|resources| RES["valuation://methods · valuation://standards"]
  SRV -.-> DOCS
src/
├── constants.py            # standards references
├── valuation/              # DCF, NAV, CCA, asset standards (relief-from-royalty, MPEEM, residual)
├── cost_of_capital/        # WACC, FF5, KMV
├── credit_risk/            # ECL, PD/LGD, CVA/DVA
├── derivatives/            # options, swaps, convertible bonds, structured products, fixed income
└── output/                 # shared result envelope (value + statistics + citations)
mcp_server/
├── method_spec.py + method_spec_seed.py   # single source of truth for tools/methods
├── standards.py                           # taxonomy loader, citations, coverage
├── tool_surface.py · engine.py · handlers.py · server.py
├── docs.py · rest.py · openapi.py · asgi.py
└── catalog.py · prompts.py                # valuation:// resources + guided prompts
standards/                                 # taxonomy.json · source/*.md · provenance · baseline
scripts/                                   # check_conformance.py · gen_docs.py

Request lifecycle

sequenceDiagram
    autonumber
    participant C as MCP / REST / Python caller
    participant E as validation + dispatch
    participant R as method-spec registry
    participant S as standards taxonomy
    participant M as computation modules (src/)
    participant O as output envelope
    C->>E: calculate_*(...) / POST /v1/calculate/*
    E->>R: resolve method + validate parameters (no defaults)
    R-->>E: method spec
    E->>M: dispatch to valuation / cost_of_capital / credit_risk / derivatives
    E->>S: attach solution_type + citations
    M-->>E: value + steps + assumptions
    E->>O: ok() / error() envelope (value · statistics · citations)
    O-->>C: status · method · value · statistics · assumptions · citations · disclaimer

Development

CI and deployment run through the APG workbench pipeline.

Requirements

  • Python 3.10+
  • pandas, numpy, scipy, python-dateutil
  • fastmcp (MCP extra), openpyxl, pdfplumber, python-docx
  • QuantLib, pandas_datareader, pytesseract/easyocr (all optional, with native fallbacks)

Documentation

Standards compliance

  • IVS 2025 — International Valuation Standards (103, 105, 210, 220, 230, 300, 400, 410, 500)
  • IFRS 13 Fair Value Measurement; IFRS 9 Financial Instruments; IFRS 16 Leases; IFRS 17 Insurance Contracts
  • IAS 36 Impairment of Assets; IAS 19 Employee Benefits; IAS 37 Provisions, Contingent Liabilities and Contingent Assets

License

Released under the MIT License.

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