manifoldbt-mcp 0.2.0

Model Context Protocol (MCP) server for the manifoldbt backtesting engine

manifoldbt-mcp

Releasing to PyPI is fully automated via GitHub Actions + PyPI Trusted Publishing — just tag vX.Y.Z and push.

Model Context Protocol (MCP) server for manifoldbt — the Rust-powered backtesting engine for quantitative research.

Expose the full manifoldbt backtesting engine to any MCP-compatible client (Claude Desktop, Claude Code, Cursor, Devin, VS Code, Windsurf, …). Let an LLM author strategies, run backtests, sweep parameters, and generate tearsheets — without the user writing a line of Python.

This package is not affiliated with the manifoldbt authors; it is a thin adapter that imports the published manifoldbt wheel and wraps its Python API behind an MCP server. All heavy lifting is done by the underlying Rust engine.

---

Install

pip install manifoldbt-mcp

This pulls in manifoldbt and the reference mcp Python SDK as dependencies. Python 3.10+ is required.

Optional extras:

pip install "manifoldbt-mcp[plot]"   # plotly stack, for plot_tearsheet
pip install "manifoldbt-mcp[dev]"    # pytest, ruff, plotly stack

Run

manifoldbt-mcp                          # stdio transport (default)
manifoldbt-mcp --http --port 8765       # streamable-HTTP transport
manifoldbt-mcp --http --host 0.0.0.0    # remote deployment

---

Client configuration

Claude Desktop

~/Library/Application Support/Claude/claude_desktop_config.json (or %APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "manifoldbt": {
      "command": "manifoldbt-mcp"
    }
  }
}

Claude Code

claude mcp add manifoldbt -- manifoldbt-mcp

Cursor / Windsurf

{
  "mcpServers": {
    "manifoldbt": {
      "command": "manifoldbt-mcp"
    }
  }
}

VS Code (Continue, Cline, …)

Any MCP-over-stdio client will work — just point it at the manifoldbt-mcp binary that pip installed on your PATH.

Remote (HTTP)

manifoldbt-mcp --http --host 0.0.0.0 --port 8765

Then connect your MCP client to http://<host>:8765/mcp.

---

What is exposed

20 tools

Tool	Purpose
get_version	Return manifoldbt version + license tier (Community / Pro).
activate_license	Activate a Pro license key for the running process.
list_indicators	Enumerate all 45+ indicators with their Python signatures.
list_examples, get_example	Browse / fetch bundled example strategies.
list_symbols, resolve_symbol	Inspect a DataStore, resolve tickers.
ingest_data	Pull bars from Binance / Hyperliquid (Databento / Massive on Pro).
build_strategy	Compile a Python DSL snippet → StrategyDef JSON + params.
validate_strategy	Validate a StrategyDef JSON through the Rust compiler.
run_backtest	Run a single backtest and return metrics + summary.
run_batch	Run many strategies in parallel on a shared data load.
run_sweep	Cartesian parameter sweep, ranked by any metric. device="auto" dispatches large grids to the GPU.
run_sweep_2d	2-D parameter heatmap.
run_walk_forward	Walk-forward optimisation (Pro).
run_stability	Parameter stability analysis.
run_monte_carlo	Trade-permutation Monte Carlo on a fresh backtest.
run_stochastic	SDE path simulation (GBM, Heston, Merton, GARCH-JD, or custom).
run_portfolio	Multi-strategy portfolio with risk rules & rebalancing.
plot_tearsheet	Render a full tearsheet as an interactive plotly HTML report from a backtest.

3 resources + 1 template

URI	Content
manifoldbt://reference/api	Quick API tour.
manifoldbt://reference/indicators	Full indicator reference (Markdown).
manifoldbt://reference/strategy-authoring	The strategy authoring guide shipped with manifoldbt.
manifoldbt://examples/{filename}	Source of a bundled example (e.g. 01_trend_following.py).

2 prompts

• write_strategy(description, universe="BTC-USDT:perp") — scaffold a new strategy from a natural-language description.
• analyze_result(metrics_json) — turn a run_backtest metrics payload into a research note.

---

Ergonomics

Every tool is JSON-friendly so LLM clients don't have to think about types:

• Dates — ISO strings: "2022-01-01" or "2022-01-01 09:30:00".
• Intervals — shorthand: "1h", "5m", "15s", "1d".
• Fees — preset names: "binance_perps", "binance_spot", "zero"; or a dict with individual overrides.
• Slippage — {"kind": "fixed_bps", "bps": 2}, {"kind": "volume_impact", "impact_coeff": 0.1, "exponent": 1.5}, etc.

Writing a strategy from a client

Strategies are authored as Python DSL snippets executed in a namespace that pre-imports every manifoldbt symbol — so the LLM never has to write import statements:

fast = ema(close, 12)
slow = ema(close, 26)

strategy = (
    Strategy.create("ema_cross")
    .signal("fast", fast)
    .signal("slow", slow)
    .size(when(fast > slow, lit(0.5), lit(0.0)))
    .stop_loss(pct=3.0)
)

Or, if the client prefers, send the already-serialised StrategyDef JSON through strategy_json on any tool.

Example run_backtest payload

{
  "strategy_code": "fast = ema(close, 12)\nslow = ema(close, 26)\nstrategy = Strategy.create('ema_cross').signal('fast', fast).signal('slow', slow).size(when(fast > slow, lit(0.5), lit(0.0))).stop_loss(pct=3.0)",
  "config": {
    "universe": {"binance": ["BTC-USDT:perp"]},
    "start": "2022-01-01",
    "end": "2024-01-01",
    "bar_interval": "1h",
    "initial_capital": 10000,
    "fees": "binance_perps",
    "slippage": {"kind": "fixed_bps", "bps": 2},
    "warmup_bars": 60
  },
  "store": {
    "data_root": "data",
    "metadata_db": "metadata/metadata.sqlite"
  }
}

---

Typical LLM session

1. list_indicators → discover available indicators.
2. Use the write_strategy prompt → draft a strategy.
3. build_strategy(strategy_code=…) → validated StrategyDef + declared parameters.
4. run_backtest(…) → metrics + summary.
5. run_sweep(…) ranked by Sharpe → find the best variant.
6. plot_tearsheet(…) → HTML tearsheet report on disk.
7. Use the analyze_result prompt → research note.

---

Security

• The server runs with the permissions of the launching process.
• strategy_code is executed in-process by the Python interpreter — there is no sandbox. The DSL namespace is convenient (pre-imported manifoldbt symbols), but nothing prevents arbitrary Python from running. Only use this server with trusted clients, or run it isolated (dedicated venv, container, restricted user).
• No inbound network unless you explicitly enable --http.

---

Development

git clone https://github.com/ClementG91/manifoldbt-mcp
cd manifoldbt-mcp
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest
ruff check .

---

Related

• Upstream engine: https://github.com/Jimmy7892/manifoldbt
• Model Context Protocol: https://modelcontextprotocol.io
• Reference Python SDK: https://github.com/modelcontextprotocol/python-sdk

---

License

MIT — see LICENSE.