# I built an MCP server that gives AI agents exact, high-precision finance math

> Source: <https://dev.to/datanestdigital/i-built-an-mcp-server-that-gives-ai-agents-exact-high-precision-finance-math-40pl>
> Published: 2026-08-10 11:13:46+00:00

LLMs are shockingly bad at arithmetic. Ask an agent to chain a CAC payback with a churn-adjusted LTV, convert it to EUR, and discount three years of cash flows, and you will get an answer that *looks* right and is quietly wrong. Floating point, dropped steps, and confident hallucination are a bad combination when the output is a number someone makes a decision on.

So I built **PrecisionCalc MCP** — a deterministic [Model Context Protocol](https://modelcontextprotocol.io) server that gives AI agents a calculator they can actually trust. Every monetary/financial value is computed with arbitrary-precision decimals (**never floats**), and every response includes the exact value, the **formula used**, the **inputs**, the unit, and any assumptions — so the agent (and you) can audit it.

It's live, free to start, and takes about 30 seconds to add.

It's a remote server over Streamable HTTP — no install:

```
https://precisioncalc-mcp.pages.dev/mcp
```

**Cursor** (`~/.cursor/mcp.json`

) or any generic client:

```
{
  "mcpServers": {
    "precisioncalc": {
      "url": "https://precisioncalc-mcp.pages.dev/mcp"
    }
  }
}
```

**Claude Desktop** (uses the `mcp-remote`

bridge):

```
{
  "mcpServers": {
    "precisioncalc": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://precisioncalc-mcp.pages.dev/mcp"]
    }
  }
}
```

Also works with VS Code, Windsurf, Cline, Zed, and anything speaking MCP. It's listed in the official MCP Registry as `io.github.inity13/precisioncalc-mcp`

.

11 tools, all returning the same clean, parseable envelope:

A call to `net_present_value`

with `rate=0.10, cashflows=[-10000, 3000, 4200, 6800]`

returns:

```
{
  "status": "success",
  "value": "1307.2877535687...",
  "formatted_value": "$1,307.29",
  "formula": "NPV = sum(CF_t / (1 + rate)^t) for t = 0..n",
  "inputs_used": { "rate": "0.10", "cashflows": ["-10000","3000","4200","6800"] },
  "unit": "USD",
  "notes": ["Period 0 cashflow is not discounted.", "..."]
}
```

The full-precision `value`

is serialized as a string so no precision is lost in JSON transport. The engine is pure and deterministic — same inputs, same output, every time. It ships with a unit-test suite plus Hypothesis property tests that assert invariants like PV↔FV round-trips and NPV(IRR) ≈ 0.

`decimal`

module and the official MCP SDK. Runs over stdio or streamable HTTP, with optional API-key auth, rate limiting, structured logging, and OpenTelemetry.`decimal.js`

— verified with 17/17 exact output parity against the Python implementation. That's what powers the free public endpoint.When an agent hits the free limit, the tool returns a structured error containing the checkout URL — so an autonomous agent can surface the paywall and the user is two clicks from a key. Prefer to self-host? The whole thing is MIT-licensed with a Docker image and Fly.io/Render blueprints — run it with unlimited calls and your own keys.

`llms.txt`

: If you build agents that touch money, give it a try and tell me what tool you'd want next. I'm considering bond pricing, WACC, and options (Black-Scholes).