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Precisioncalc MCP Server

by Inity13
Developer ToolsModerate5.2MCP RegistryLocalRemote
Free

Server data from the Official MCP Registry

High-precision finance & business calculations for AI agents — exact decimals, never floats.

About

High-precision finance & business calculations for AI agents — exact decimals, never floats.

Remote endpoints: streamable-http: https://precisioncalc-mcp.pages.dev/mcp

Security Report

5.2
Moderate5.2Moderate Risk

PrecisionCalc MCP is a well-architected financial calculation server with proper authentication, rate limiting, and input validation. Code quality is high with comprehensive error handling and no malicious patterns detected. Minor concerns around async error handling in the Worker build and env var exposure in error messages do not significantly impact security given the server's purpose and safeguards. Supply chain analysis found 7 known vulnerabilities in dependencies (0 critical, 6 high severity).

4 files analyzed · 11 issues found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

HTTP Network Access

Connects to external APIs or services over the internet.

env_vars

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system_info

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How to Install & Connect

Available as Local & Remote

This plugin can run on your machine or connect to a hosted endpoint. during install.

Documentation

View on GitHub

From the project's GitHub README.

PrecisionCalc MCP

A deterministic Model Context Protocol (MCP) server that gives LLM agents reliable, high-precision business, finance, and operational calculations.

LLMs routinely lose precision or hallucinate on multi-step financial formulas, currency conversions, business-day logic, and growth math. PrecisionCalc offloads that work to exact, transparent tools. Every monetary/financial value is computed with Python's decimal module (never floats), and every result is returned in a consistent, agent-parseable JSON envelope that includes the exact value, a human-readable value, the formula applied, the inputs used, the unit, and any assumptions/warnings.

v2 highlights: live + historical FX (ECB), 14 SaaS metrics, NPV/IRR, loan amortization, depreciation, a batch_calculate tool, per-country holidays, API-key auth + rate limiting + usage metering on the HTTP transport, structured JSON logging, optional OpenTelemetry tracing, and property-based tests.

🌐 Live hosted server (free, no install)

A public remote MCP server runs on Cloudflare's edge — point any Streamable-HTTP MCP client at it:

https://precisioncalc-mcp.pages.dev/mcp
{ "mcpServers": { "precisioncalc": {
    "type": "http", "url": "https://precisioncalc-mcp.pages.dev/mcp" } } }

The edge build (worker-src/) is a Cloudflare Pages Function that mirrors the Python engine using decimal.js — verified 17/17 exact output parity. Landing page + docs: https://precisioncalc-mcp.pages.dev.

Plans (hosted endpoint)

PlanPriceDaily callsLive/historical FXbatch_calculate
Free (no key)$015 / day (per IP)❌ static only
Starter$12/mo5,000 / day
Pro$39/mo50,000 / day

Checkout is Stripe (subscription). On success you get an API key instantly; send it as X-API-Key: <key> (or Authorization: Bearer <key>). Manage/cancel at /portal. When a limit is hit, tools return a structured status:"error" envelope with type, usage, and an upgrade block containing checkout URLs — so an agent can surface the paywall to the user and act on it. Self-host (below) for unlimited calls with your own keys.

Billing internals live in worker-src/billing.mjs (Stripe REST + Cloudflare KV for keys and daily counters). Server env: STRIPE_SECRET_KEY, STRIPE_WEBHOOK_SECRET, PRICE_STARTER, PRICE_PRO, FREE_DAILY, STARTER_DAILY, PRO_DAILY, and a PRECISIONCALC_KV namespace binding (see wrangler.toml).

Rebuild/redeploy the edge server:

npm install          # decimal.js + esbuild
npm run deploy       # bundles worker-src -> site/_worker.js and deploys to Pages

What it does

11 tools, all returning a uniform structured response:

ToolPurpose
calculate_metric14 SaaS/business metrics (LTV, CAC, churn, MRR growth, NRR, GRR, Rule of 40, magic number, break-even, ...)
currency_convertConvert 9 major currencies; static (offline) or live/historical ECB rates
business_daysAdd/count business days, next/previous; US/UK/EU + any ISO country + custom holidays
compound_growthFuture value, present value, CAGR; 7 compounding frequencies incl. continuous
net_present_valueNPV / discounted cash flow of a cashflow series
internal_rate_of_returnIRR (Newton + bisection fallback)
loan_amortizationLevel-payment loan: payment, totals, full schedule, extra-payment payoff
depreciationstraight-line / declining-balance / sum-of-years-digits schedules
batch_calculateRun many calculations in one request
list_metricsDiscovery: every metric with descriptions + required params
health_checkServer status, version, capabilities

Consistent response envelope

Success:

{
  "status": "success",
  "value": "1600",                       // exact, full-precision (string for money/rates)
  "formatted_value": "$1,600.00",        // human-readable
  "formula": "LTV = (ARPU * gross_margin) / churn_rate",
  "inputs_used": { "arpu": "100", "gross_margin": "0.8", "churn_rate": "0.05" },
  "unit": "USD",
  "notes": ["LTV = (ARPU x gross_margin) / churn_rate.", "..."]
}

Error (never raised across the tool boundary):

{
  "status": "error",
  "error": {
    "type": "missing_parameter",
    "message": "Missing required parameter 'churn_rate'.",
    "hint": "Include 'churn_rate' in params. See list_metrics for the full schema."
  }
}

Project structure

precisioncalc-mcp/
├── server.py                 # MCP server: tool definitions + transports
├── security.py               # API-key auth + token-bucket rate limit + metering (ASGI)
├── observability.py          # Structured JSON logging + optional OpenTelemetry
├── requirements.txt / pyproject.toml
├── Dockerfile / .dockerignore
├── fly.toml / render.yaml    # One-click hosting configs
├── .env.example
├── calculations/
│   ├── _util.py              # Decimal coercion, validation, formatting
│   ├── metrics.py            # 14 business/SaaS metrics + catalog
│   ├── currency.py           # FX: static + Frankfurter (live/historical) providers
│   ├── business_days.py      # Region-aware holidays (built-in + `holidays` lib)
│   ├── growth.py             # FV / PV / CAGR
│   └── finance.py            # NPV / IRR / loan amortization / depreciation
├── schemas/responses.py      # Response envelope helpers
├── examples/agent_example.py # End-to-end MCP client demo
├── site/                     # Static landing/docs page (Cloudflare Pages)
└── tests/                    # 49 unit tests + Hypothesis property tests

Requirements

  • Python 3.11+ (developed/tested on 3.12)
  • Core: mcp, python-dateutil
  • Recommended: uvicorn + starlette (HTTP transport), holidays (per-country calendars)
  • Optional: opentelemetry-sdk (tracing), pytest + hypothesis (tests)

The server auto-detects the SDK layout and works with mcp >= 2.0 (MCPServer), mcp 1.x (FastMCP), or the standalone fastmcp package.


Run it locally

cd precisioncalc-mcp
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt          # or: pip install -e ".[all]"

# stdio transport (default; how MCP clients launch it)
python server.py            # or: precisioncalc-mcp   (console entrypoint)

# Streamable HTTP transport (endpoint: /mcp)
python server.py http
PRECISIONCALC_API_KEYS=key1,key2 PRECISIONCALC_FX_PROVIDER=frankfurter python server.py http

Demo + tests:

python examples/agent_example.py         # live end-to-end over stdio
python tests/test_calculations.py        # 28 core tests (no pytest needed)
python tests/test_v2.py                  # 17 v2 tests
python tests/test_properties.py          # Hypothesis property tests
# or simply:  pytest -q

Register with an MCP client (stdio)

{ "mcpServers": { "precisioncalc": {
    "command": "python", "args": ["/absolute/path/to/precisioncalc-mcp/server.py"] } } }

Deploy

Docker

docker build -t precisioncalc-mcp .
docker run --rm -p 8000:8000 -e PRECISIONCALC_API_KEYS=your-key precisioncalc-mcp
docker run --rm -i precisioncalc-mcp python server.py stdio

Fly.io

fly launch --no-deploy
fly secrets set PRECISIONCALC_API_KEYS=key1,key2
fly deploy

Render.com

Push to GitHub, then New + → Blueprint and point at the repo (render.yaml). Set PRECISIONCALC_API_KEYS as a secret in the dashboard.


Configuration (env vars)

VarDefaultPurpose
PRECISIONCALC_HOST / PRECISIONCALC_PORT127.0.0.1 / 8000HTTP bind
PRECISIONCALC_API_KEYS(empty)Comma-separated keys. Empty = open mode (still metered/limited by IP)
PRECISIONCALC_RATE_LIMIT_PER_MIN / _BURST120 / 40Token-bucket limits
PRECISIONCALC_METRICS_PATH/metricsUsage-metrics endpoint
PRECISIONCALC_FX_PROVIDERstaticstatic or frankfurter (live/historical ECB)
PRECISIONCALC_FX_TTL / _TIMEOUT3600 / 4FX cache TTL / HTTP timeout (s)
PRECISIONCALC_LOG_LEVEL / _LOG_JSONINFO / 1Logging
PRECISIONCALC_OTEL01 enables OpenTelemetry tracing if SDK present

Tools & parameters

calculate_metric(metric, params, currency="USD")

Rates/margins are decimals (0.05 = 5%).

metricparamsunit
ltvarpu, churn_rate, gross_margin(=1)currency
cactotal_spend, new_customerscurrency
ltv_cac_ratioltv, cacratio
payback_period_monthscac, monthly_revenue_per_customer, gross_margin(=1)months
contribution_marginrevenue, variable_costscurrency
gross_marginrevenue, cogspercent
churn_ratecustomers_lost, customers_at_startpercent
mrr_growth_ratebeginning_mrr, ending_mrrpercent
arrmrrcurrency
break_even_unitsfixed_costs, price_per_unit, variable_cost_per_unitunits
nrrstarting_mrr, expansion_mrr, contraction_mrr, churned_mrrpercent
grrstarting_mrr, contraction_mrr, churned_mrrpercent
rule_of_40growth_rate, profit_marginpercent
magic_numbercurrent_quarter_revenue, prior_quarter_revenue, prior_quarter_sm_spendratio

currency_convert(amount, from_currency, to_currency, date=None, live=None)

USD, EUR, GBP, JPY, CAD, AUD, CHF, CNY, INR. date (YYYY-MM-DD) or live=true uses live/historical ECB rates (frankfurter.app), with automatic static fallback on any network failure. Returns rate, provider, is_live, and timestamps.

business_days(operation, start_date, days=None, end_date=None, region="US", custom_holidays=None)

operation: add_business_days | count_business_days (inclusive) | next_business_day | previous_business_day. region: US | UK | EU | NONE, or any ISO country code when the holidays package is installed (DE, FR, CA, AU, JP, IN, ...).

compound_growth(operation, rate, years, present_value, future_value, begin_value, end_value, compounding="annually", currency="USD")

operation: future_value | present_value | cagr. compounding: daily | weekly | monthly | quarterly | semiannually | annually | continuous.

net_present_value(rate, cashflows, currency="USD")

NPV = Σ CFₜ/(1+rate)ᵗ. cashflows[0] = period 0 (usually the negative outlay).

internal_rate_of_return(cashflows, guess=0.1)

Per-period rate where NPV = 0. Requires a sign change in the cashflows.

loan_amortization(principal, annual_rate, term_months, extra_payment=0, currency="USD", include_schedule=false)

Returns monthly payment, months-to-payoff, total interest, total paid, and (optionally) the full month-by-month schedule.

depreciation(method, cost, salvage_value, useful_life_years, currency="USD")

method: straight_line | declining_balance | sum_of_years_digits. Returns the full yearly schedule; book value converges to salvage_value.

batch_calculate(calls)

calls: list of {"tool": <name>, "arguments": {...}} (max 100). One item failing never aborts the batch.

list_metrics() / health_check()

Discovery + status. No parameters.


Example MCP tool-call payloads

{ "name": "calculate_metric",
  "arguments": { "metric": "rule_of_40", "params": { "growth_rate": 0.30, "profit_margin": 0.15 } } }
{ "name": "currency_convert",
  "arguments": { "amount": 5000, "from_currency": "EUR", "to_currency": "GBP", "date": "2024-01-15" } }
{ "name": "net_present_value",
  "arguments": { "rate": 0.10, "cashflows": [-10000, 3000, 4200, 6800] } }
{ "name": "loan_amortization",
  "arguments": { "principal": 250000, "annual_rate": 0.065, "term_months": 360, "include_schedule": false } }
{ "name": "batch_calculate",
  "arguments": { "calls": [
    { "tool": "internal_rate_of_return", "arguments": { "cashflows": [-10000, 3000, 4200, 6800] } },
    { "tool": "depreciation", "arguments": { "method": "declining_balance", "cost": 50000, "salvage_value": 5000, "useful_life_years": 5 } }
  ] } }

Design decisions & assumptions

  • Decimal everywhere money/rates matter; value is serialized as a string to prevent float loss in JSON, with a separate pretty formatted_value. Precision = 50 sig figs.
  • Rates/margins are decimals (0.05 = 5%), documented in every tool.
  • FX: static USD-based table (as_of 2024-06-01) is the offline default; frankfurter provider adds live + historical ECB rates with in-memory TTL cache and graceful static fallback.
  • Business days: holidays computed per-year (floating US, Easter-based UK/EU); count is inclusive; add accepts negatives; custom holidays unioned; any ISO country via holidays lib.
  • IRR uses Newton's method with a bracketed bisection fallback; requires a sign change.
  • Errors never cross the tool boundary as exceptions — always status:"error" with a machine type + actionable hint.
  • HTTP hardening is opt-in via env: API keys, token-bucket rate limiting, /metrics usage.
  • SDK compatibility shim runs on mcp>=2.0, mcp 1.x, or standalone fastmcp unchanged.

Monetization hooks

  • AuthPRECISIONCALC_API_KEYS; requests need X-API-Key or Authorization: Bearer.
  • Rate limiting — per-key token bucket (per-IP in open mode); swap for Redis to scale.
  • Usage metering — in-memory counters exposed at /metrics; the seam for per-key billing.
  • FX providercalculations/currency.py::RateProvider is the drop-in point for a licensed feed.

Roadmap (post-v2)

  1. Redis-backed rate limiting + billing-grade usage metering.
  2. Persisted historical FX + more providers; multi-currency carry through metrics.
  3. Bond pricing/yield, WACC, options (Black-Scholes), tax/VAT, unit conversions.
  4. Prometheus exporter + Grafana dashboard alongside OTel traces.
  5. Published PyPI package + Docker image on GHCR; hosted multi-tenant SaaS.

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