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FLOX MCP Server

FLOX MCP Server

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github.com/FLOX-FoundationDeveloper Tools229Updated today

About FLOX MCP Server

FLOX MCP Server is an MCP server for the FLOX trading framework, aimed at quant researchers and strategy developers using Claude or Cursor. It exposes about 30 tools covering backtests, strategy scaffolding, lookahead bias validation, indicators, order placement and PnL queries.

Key features

  1. 01About 30 MCP tools
  2. 02Backtest execution
  3. 03Strategy scaffolding
  4. 04Lookahead bias validation
  5. 05Technical indicator computation
  6. 06Order placement and PnL queries

Use cases

  1. 01Running strategy backtests from Claude or Cursor
  2. 02Scaffolding and validating new trading strategies
  3. 03Querying orders and profit and loss

MCP support

This project is an MCP server that exposes FLOX framework tools for backtesting, strategies, indicators and trading to MCP clients such as Claude and Cursor.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

CI Docs PyPI npm

lrvx

lrvx is a framework for building trading systems.

Strategies, backtests, paper trading, and live execution sit behind one toolkit. AI agents discover the surface and drive it end-to-end through an MCP control plane. One strategy class runs backtest, paper, and live. Bindings for Python, Node.js, Codon, embedded JavaScript, and a stable C API.

Documentation is available at docs.lrvx.dev

Language bindings

LanguageInstallDocs
Pythonpip install lrvxreference
Node.jsnpm install @lrvx/lrvxreference
Codonbuild from sourcereference
JavaScript (embedded)bundled with C++ buildreference
C APIliblrvx_capi.soreference

All bindings expose the same strategy API, indicators, order books, backtesting, and data I/O. The C API is the integration point for adding support for any other language.

AI companion

lrvx-mcp is a Model Context Protocol server that gives AI coding agents (Cursor, Claude Code, Cline) grounded access to the lrvx surface — symbol lookups across bindings, scaffolders, indicator and backtest tools, full-text doc search.

pip install lrvx-mcp

See the package README for setup and the full tool list.

Connectors

Native exchange connectors (Bybit, Bitget, Hyperliquid, Polymarket) live under connectors/ and build with -DLRVX_BUILD_CONNECTORS=ON. The flag defaults to OFF so a backtest-only or research build doesn't pay the dependency cost. See the connectors README for adapter notes and the optional Polymarket Rust toolchain step.

Venue

Beyond trading on a market, lrvx can be one: the optional venue/ module adds a matching engine and the machinery around it, so many participants can trade against a single order book. See the venue docs.

Build options

The CMake options that gate every optional artefact (bindings, demo, tools, tests, benchmarks, connectors) are catalogued in docs/build/feature-flags.md. Defaults are OFF so a bare cmake -B build produces only the core C++ static library.

Commercial Services

For commercial support, enterprise connectors, and custom development, visit lrvx.dev/services.

Contributing

Contributions are welcome via pull requests. Please follow the existing structure and naming conventions. Tests, benchmarks, and documentation should be included where appropriate. Code style is enforced via clang-format. See contributing guide.

License

lrvx is licensed under the MIT License. See LICENSE for details.

Disclaimer

lrvx is provided "as is" without warranty of any kind, express or implied. The authors are not liable for any damages or financial harm arising from its use, including trading losses or system failures. This software is intended for educational and research purposes. Production use is at your own risk. See DISCLAIMER.md for full legal notice.

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.