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Fair Value MCP Server

Fair Value MCP Server

Remote
github.comFinance1Updated 1d ago

About Fair Value MCP Server

A financial valuation system for analysts and AI agents, available as an MCP server, REST API, and Python library. Its methods cover corporate and intangible valuation, with results linked 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 covering 135 methods. It supports stdio and Streamable HTTP, and provides machine-readable method and standards catalogs.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://fair-value.ascent-partners.com/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Packages
pypifair-value0.2.6

Tools

14
ParameterTypeRequiredDescription
methodenumYesFormula to apply; the tool description lists the exact inputs each value requires.ifrs17_gmm | ifrs17_paa | ifrs17_vfa | ias19_puc | ias37_provision
periodsanyNoNumber of periods n (>=1).
outcomesanyNoOutcome values aligned with probabilities.
premiumsanyNoPremiums in reporting currency.
cash_flowsanyNoProjected cash flows in reporting currency, indexed t=1..n.
claims_cashanyNoExpected claims in reporting currency.
discount_rateanyNoDiscount rate as a decimal (0.10 = 10%); pre-tax when method=viu_pre_tax.
probabilitiesanyNoCumulative success probability per period in [0,1], aligned with cash_flows.
risk_adjustmentanyNoExplicit risk adjustment for non-financial risk.
coverage_periodsanyNoCoverage periods for the PAA (>=1).
attribution_yearsanyNoYears of service for attribution (>=1).
projected_benefitsanyNoProjected benefits per service year.
acquisition_cash_flowsanyNoAcquisition cash flows in reporting currency.
underlying_items_returnanyNoReturn on underlying items (decimal).

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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