MetaTrader 5 MCP Server

MetaTrader 5 integration for Model Context Protocol (MCP). Provides read-only access to MT5 market data through Python commands, built on the FastMCP v3 framework. Optionally encodes responses in TOON for LLM token savings.

⚡ What's New in v0.6.3

• Production packaging fix – The wheel now depends on fastmcp-slim[server]>=3.4,<4 directly instead of the empty fastmcp meta-package. This bypasses a known FastMCP v3 packaging hazard that could leave the fastmcp namespace empty after a pip upgrade. Also: MetaTrader5 is now platform-gated to Windows, so the wheel installs cleanly on Linux/macOS too (with a runtime guard that explains why the tools can't run there).

Related MCP server: MetaTrader5 MCP Server

⚡ What's New in v0.6.2

• TOON output encoding – tabular tool responses (50-bar copy_rates_from_pos, etc.) are automatically encoded in TOON format when it would save ≥10 % of tokens over compact JSON. The LLM never has to ask for it; the server detects the shape. Install with pip install "mt5-mcp[toon]".

⚡ What's New in v0.6.0

• FastMCP v3 – Pinned fastmcp>=3.0,<4. The framework derives JSON schemas from your Python type annotations; no more string-encoded JSON parameters.

• No more Gradio – the experimental gradio_server.py and the [ui] extra are gone. This release is FastMCP-only.

• Three transports in one process – stdio for Claude Desktop / VS Code, Streamable HTTP on /mcp, and legacy SSE. --transport all runs them concurrently.

• ASGI deployment – --asgi emits an app:app for uvicorn, gunicorn, or hypercorn.

• First-class middleware – rate limiting (per-IP sliding window) and request logging live in FastMCP middleware, not in function bodies.

• CI – lint (ruff) + cross-platform pytest jobs on every push.

• 61 tests – unit + integration via fastmcp.Client; CI runs them on Windows + Linux.

# Default: stdio (Claude Desktop, VS Code, etc.)
python -m mt5_mcp
# or:
mt5-mcp

# Streamable HTTP with rate limiting
python -m mt5_mcp --transport http --host 0.0.0.0 --port 8000 --rate-limit 30

# All transports at once
python -m mt5_mcp --transport all --port 8000

# ASGI app for production (mount behind Uvicorn / Gunicorn / Hypercorn)
python -m mt5_mcp --asgi

📖 Documentation:

• USAGE.md — Comprehensive instructions, tool reference, troubleshooting.

• CHANGELOG.md — Release history and migration notes from v0.5.x.

• docs/v0.6.0-architecture.md — Architecture rationale.

Key Capabilities

• Read-only MT5 bridge — Safe namespace exposes only data-retrieval APIs and blocks all trading calls.

• Transaction history access — history_deals_get, history_orders_get, positions_get.

• Multiple interaction models — Write Python (mt5_execute), submit structured MT5 queries (mt5_query), or run full analyses with indicators, charts, and forecasts (mt5_analyze).

• Technical analysis toolkit — ta, numpy, matplotlib ship in the namespace for RSI, MACD, Bollinger Bands, multi-panel charts, and more.

• Forecasting + ML signals — Prophet forecasting and optional XGBoost buy/sell predictions with confidence scoring.

• LLM-friendly guardrails — Clear tool descriptions, runtime validation, and result-assignment reminders keep assistant output predictable.

Available Tools

mt5_query

Structured JSON interface that maps directly to MT5 read-only operations with automatic validation, timeframe conversion, and friendly error messages.

{
  "operation": "copy_rates_from_pos",
  "symbol": "BTCUSD",
  "parameters": {"timeframe": "H1", "count": 100}
}

Tip: parameters is a real JSON object in v0.6.0 — no more string-encoded JSON like the v0.5.x "parameters": "{\"timeframe\":\"H1\"}" form.

mt5_analyze

Pipeline tool that chains a query → optional indicators → charts and/or Prophet forecasts (with optional ML signals) in one request.

{
  "query": {
    "operation": "copy_rates_from_pos",
    "symbol": "BTCUSD",
    "parameters": {"timeframe": "D1", "count": 180}
  },
  "indicators": [
    {"function": "ta.trend.sma_indicator", "params": {"window": 50}},
    {"function": "ta.momentum.rsi", "params": {"window": 14}}
  ],
  "forecast": {"periods": 30, "plot": true, "enable_ml_prediction": true}
}

mt5_execute

Free-form Python execution inside a curated namespace. Ideal for quick calculations, prototyping, and bespoke formatting.

rates = mt5.copy_rates_from_pos('BTCUSD', mt5.TIMEFRAME_H1, 0, 100)
df = pd.DataFrame(rates)
df['RSI'] = ta.momentum.rsi(df['close'], window=14)
result = df[['time', 'close', 'RSI']].tail(10)

Prerequisites

• Windows OS (MetaTrader5 library is Windows-only)

• MetaTrader 5 terminal installed and running

• Python 3.10+

Installation

git clone <repository-url>
cd MT5-MCP
pip install -e .

Optional extras:

# Everything
pip install -e .[all]

The TOON token-efficient output encoder is not bundled in this release — the upstream toon-format/toon-python package has no PyPI wheel, and PyPI rejects direct-URL dependencies. The TOON heuristic in mt5_mcp.toon_output silently falls back to JSON when the encoder isn't installed. Vendoring the encoder is tracked for v0.6.4.

This installs:

• fastmcp — MCP server framework (built on the official mcp SDK)

• MetaTrader5 — official MT5 Python library

• pandas, numpy, matplotlib, ta — data + charting

• prophet, xgboost, scikit-learn — forecasting + ML signals

• uvicorn, starlette, httpx — HTTP transport

• pydantic — request/response validation

Configuration

Claude Desktop (stdio)

Add to your Claude Desktop configuration file at %APPDATA%\Claude\claude_desktop_config.json:

{
  "mcpServers": {
    "mt5": {
      "command": "python",
      "args": ["-m", "mt5_mcp"]
    }
  }
}

Or, if mt5-mcp is on your PATH:

{
  "mcpServers": {
    "mt5": {
      "command": "mt5-mcp",
      "args": []
    }
  }
}

For logging:

{
  "mcpServers": {
    "mt5": {
      "command": "python",
      "args": ["-m", "mt5_mcp", "--log-file", "C:\\path\\to\\mt5_mcp.log"]
    }
  }
}

HTTP MCP Clients

{
  "mcpServers": {
    "mt5-http": {
      "url": "http://localhost:8000/mcp/"
    }
  }
}

Works with MCP Inspector, Claude Desktop (HTTP mode), VS Code extensions, and any remote deployment.

ASGI / Production

python -m mt5_mcp --asgi          # prints "app:app" hint
uvicorn mt5_mcp.__main__:app --host 0.0.0.0 --port 8000 --workers 2

The lifespan context is wired in, so FastMCP startup/shutdown runs correctly under multi-worker Uvicorn.

CLI

python -m mt5_mcp [--transport stdio|http|sse|all] [--host HOST] [--port PORT]
                  [--path PATH] [--rate-limit N]
                  [--log-level LEVEL] [--log-file FILE]
                  [--asgi]

TOON Output Encoding (Token-Efficient for LLMs) — experimental

Status: the shape-detection heuristic and the format_response() entry point ship in this release, but the upstream toon-format/toon-python encoder has no PyPI wheel, so PyPI rejects our Requires-Dist: toon_format @ https://... reference. The heuristic silently falls back to JSON until the encoder is vendored (tracked for v0.6.4). The code, tests, and savings measurement below are the design target.

The server is designed to automatically encode tabular tool responses in TOON — a line-oriented, indentation-based format designed for LLM contexts. TOON declares array shapes once ([N] count + {field1,field2,...} column list) and uses indentation instead of braces, so tabular payloads (MT5 rate bars, indicator values, transaction history) are 20–40 % smaller than compact JSON.

The LLM doesn't choose. The server detects the shape and applies TOON only when it helps. There is no output_format or wire_format parameter on any tool.

Enable TOON

pip install "mt5-mcp[toon]"

This installs toon-format/toon-python — the canonical Python implementation of the TOON spec. The package's PyPI distribution is a stub; the [toon] extra pulls the real release directly from GitHub.

What the LLM sees

When the response contains a uniform array of ≥5 dicts in the data field AND encoding it as TOON saves ≥10 % of tokens vs. compact JSON, the server replaces that array with a TOON string and adds a sibling marker:

{
  "operation": "copy_rates_from_pos",
  "success": true,
  "metadata": {"symbol": "BTCUSD", "timeframe": "H1", "count": 50},
  "data_format": "toon",
  "data": "data[50]{time,close,vol}:\n  1700000000,63000.0,0\n  1700003600,63010.0,100\n  ..."
}

The "data_format": "toon" marker tells the LLM the data field is a TOON document (decode with any TOON library, or call back through mt5_query to round-trip).

When the heuristic doesn't fire — single dicts, small arrays, heterogeneous rows, error envelopes, non-tabular shapes — the response is unchanged compact JSON. No surprises.

mt5_execute is intentionally excluded from the TOON heuristic: its output is already a pre-formatted text string (markdown tables, JSON snippets, plain text) that is more universally parseable than TOON.

Measured savings (o200k_base tokens)

Tiny responses (symbol_info lookups, error envelopes) cost 1–3 extra tokens because TOON's [N] array header is overhead on objects with one or two keys. The heuristic keeps those as JSON automatically.

Architecture & Compliance

• Built on FastMCP v3 (a thin wrapper around the official mcp Python SDK).

• Safe execution namespace exposes vetted objects (mt5, datetime, pd, ta, numpy, matplotlib) while blocking trading calls and disallowed modules.

• Runtime validation catches mt5.initialize() / mt5.shutdown() attempts and highlights the correct workflow.

• Thread-safe MT5 connection management plus IP-scoped rate limiting (HTTP/SSE only).

• Three concerns live in dedicated layers:

  • mcp_server.py — the FastMCP instance and middleware wiring

  • mcp_tools.py — the three tool functions

  • middleware.py — cross-cutting rate-limit + logging

Troubleshooting

FastMCP Install Hazard (cannot import name 'FastMCP' from 'fastmcp')

If you see ImportError: cannot import name 'FastMCP' from 'fastmcp' (unknown location) when running mt5-mcp, the underlying fastmcp package directory is empty. This is a known packaging hazard in FastMCP v3 caused by pip's install/upgrade sequence — the fastmcp meta-package can wipe files written by its companion fastmcp-slim package, leaving an empty namespace.

Recovery (one-time):

pip uninstall -y fastmcp fastmcp-slim
pip install mt5-mcp

Or, if you prefer to keep using the legacy pip install --upgrade mcp workflow that triggered the issue:

pip install --force-reinstall fastmcp-slim==3.4.7

mt5-mcp v0.6.3+ depends directly on fastmcp-slim[server]>=3.4,<4, so fresh installs skip this path entirely.

MT5 Connection Issues

1. Ensure MT5 terminal is running before starting the MCP server.

2. Enable algo trading in MT5: Tools → Options → Expert Advisors → Allow automated trading.

3. Check MT5 terminal logs for any errors.

Enable Logging

python -m mt5_mcp --log-file mt5_debug.log

Or configure it in the Claude Desktop config (see Configuration above).

Common Errors

"MT5 connection error: initialize() failed"

• MT5 terminal is not running.

• MT5 is not installed.

• Algo trading is disabled in MT5.

"Symbol not found"

• Check symbol name spelling (case-sensitive).

• Symbol may not be available in your MT5 account.

• Use mt5_query with operation: symbols_get to list available symbols.

"No data returned"

• Symbol may not have historical data for the requested period.

• Check date range validity.

• Some symbols may have limited history.

"Rate limit exceeded"

• HTTP/SSE transport only. Increase --rate-limit, or set 0 to disable.

• Stdio transport is single-process and is never rate-limited.

Security

This server provides read-only access to MT5 data. Trading functions are explicitly excluded from the safe namespace:

Blocked Functions

• order_send() — Place orders

• order_check() — Check order

• positions_get() — Get positions (read-only but blocked to prevent confusion)

• positions_total() — Position count

• All order/position modification functions

Only market data and information retrieval functions are available.

License

MIT License

Contributing

Contributions are welcome! Please ensure:

1. All code follows the read-only philosophy

2. Tests pass (pytest -q)

3. Documentation is updated

4. CI lint passes (ruff check src tests)