Latamfx

An MCP server that exposes public LatAm FX data and an auditable reconciliation engine to AI agents (Claude Desktop, Claude Code, or any MCP client).

CI Python MCP Ruff Checked with mypy License: MIT

latamfx-mcp lets an LLM agent answer questions like "what's the blue dollar today?", "convert 1,500 USD to ARS at the MEP rate", or "reconcile these two ledgers and tell me what didn't match" — entirely from free, key-less public APIs. No credentials, no client data.

It doubles as a reference implementation of a production-shaped MCP server: hexagonal architecture, typed contracts, retries with backoff, a TTL cache, contract tests against mocked HTTP, CI, Docker and a Cloud Run deployment module.

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Tools

Tool	What it does
list_fx_sources	List supported sources (oficial, blue, MEP, CCL, mayorista, cripto, tarjeta).
get_fx_quote	Latest buy/sell quote for a source.
get_fx_timeseries	Historical buy/sell series (most recent N points).
get_fx_stats	min / max / mean / volatility of the mid price (computed with Polars).
convert	Convert an amount between currencies using a source's quote (USD↔ARS).
reconcile	Match two ledgers with a multi-rule engine; returns matches, misses and a match rate.

Plus a resource: fx://sources (the source catalog as text).

The reconciliation engine

reconcile is a sanitized, generic version of intercompany / bank reconciliation engines used in real fintech work. Rules run in priority order and each right-side entry is consumed at most once, so the output is a valid one-to-one assignment where every match is traceable to the rule that produced it:

1. exact_reference — same non-empty external reference (score 1.0).
2. amount_date — equal amount within a day-tolerance window (score decays with the gap).
3. fuzzy_description — equal amount + similar free-text description above a threshold.

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Architecture

Hexagonal (ports & adapters): the domain and application layers know nothing about HTTP or MCP, so the engine is pure and the data source is swappable.

flowchart TD
    Agent[AI agent / MCP client] -->|tools, resources| Server[server.py · FastMCP]
    Server --> App[application · FxService, ReconciliationService]
    App --> Domain[domain · models + reconciliation engine]
    App -->|FxProvider port| Port{{ports}}
    Port -.implemented by.-> Adapter[infrastructure · DolarApiProvider]
    Adapter -->|httpx + retries + TTL cache| Public[(dolarapi.com / argentinadatos.com)]

src/latamfx_mcp/
├── domain/           # pure models + reconciliation engine (no I/O)
├── ports/            # FxProvider Protocol (dependency inversion)
├── application/      # use cases: FX + reconciliation
├── infrastructure/   # httpx adapter, retry policy, TTL cache
├── config.py         # env-driven settings
└── server.py         # FastMCP wiring (tools + resource)

See docs/architecture.md and the ADRs for the design decisions.

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Quickstart

Requires uv.

git clone https://github.com/floreskemec/latamfx-mcp.git
cd latamfx-mcp
uv sync
uv run latamfx-mcp     # starts the MCP server over stdio

Use it from Claude Code

claude mcp add latamfx -- uv --directory /absolute/path/to/latamfx-mcp run latamfx-mcp

Use it from Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "latamfx": {
      "command": "uv",
      "args": ["--directory", "/absolute/path/to/latamfx-mcp", "run", "latamfx-mcp"]
    }
  }
}

Then ask Claude: "Using latamfx, convert 1500 USD to ARS at the blue rate and show me the last 7 days of the blue dollar."

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Development

uv sync
uv run pytest            # tests + coverage
uv run ruff check .      # lint
uv run ruff format .     # format
uv run mypy              # static types

Configuration is read from environment variables (all optional):

Variable	Default	Purpose
LATAMFX_HTTP_TIMEOUT	10.0	HTTP timeout (seconds).
LATAMFX_HTTP_RETRIES	3	Max attempts on transient failures.
LATAMFX_CACHE_TTL	60.0	Quote/series cache TTL (seconds).

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Deployment

A multi-stage Dockerfile builds a slim image, and deploy/terraform contains a minimal OpenTofu/Terraform module to run it on Google Cloud Run. See the deploy README.

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Data sources

• dolarapi.com — latest quotes.
• ArgentinaDatos — historical series.

Both are free, community-maintained public APIs. This project is not affiliated with them; please review their terms before heavy use.

License

MIT © Gonzalo Flores Kemec