What are the requirements for Jesse MCP Server?

Jesse MCP Server requires the following environment variables: JESSE_URL (optional), JESSE_PASSWORD (required), JESSE_API_TOKEN (optional). You'll also need a compatible MCP client like Claude Desktop or Claude Code.

Is Jesse MCP Server free to use?

Yes, Jesse MCP Server is open source and free to use. You can find the source code on GitHub.

What MCP clients support Jesse MCP Server?

Jesse MCP Server works with any MCP-compatible client including Claude Desktop (Anthropic's official desktop app), Claude Code (CLI tool), Cursor, and other editors with MCP support.

How do I configure Jesse MCP Server?

Configure Jesse MCP Server by adding it to your MCP client's config file. The setup block at the top of this page generates a ready-to-paste config for Claude Code, Cursor, Codex, Windsurf, and Claude Desktop.

MCP server/ai-tools

Jesse MCP Server

Q: What tools does Jesse MCP Server provide?

backtest: Run single backtest with specified parameters. optimize: Optimize hyperparameters using Optuna. monte_carlo: Monte Carlo simulations for risk analysis. strategy_list: List available strategies. risk_report: Comprehensive risk assessment.

Q: How do I install Jesse MCP Server?

Install Jesse MCP Server by running: pip install jesse-mcp

Exposes the Jesse algorithmic trading framework's capabilities to LLM agents

bkuri/jesse-mcp ↗by bkuriupdated Apr 5, 2026

Add it to Claude Code

claude mcp add -e "JESSE_PASSWORD=${JESSE_PASSWORD}" jesse-mcp -- uvx jesse-mcp

Required:JESSE_PASSWORD+ 2 optional

Make your agent remember this setup

jesse-mcp's config, env vars, and the gotchas you hit — recalled in every future Claude Code, Cursor, and Codex session.

npx conare@latest

Free · one command · indexes the sessions already on disk. Set up in the browser instead →

What it does

Single and batch backtest execution via Jesse REST API
Hyperparameter tuning with walk-forward validation
Statistical robustness testing using Monte Carlo analysis
Comprehensive risk assessment including VaR and stress testing
15 specialized agent tools for autonomous trading workflows

Tools 5

backtestRun single backtest with specified parameters

optimizeOptimize hyperparameters using Optuna

monte_carloMonte Carlo simulations for risk analysis

strategy_listList available strategies

risk_reportComprehensive risk assessment

Environment Variables

JESSE_URLJesse REST API URL

JESSE_PASSWORDrequiredJesse UI password

JESSE_API_TOKENPre-generated API token

Try it

→List all available trading strategies currently in my Jesse framework.

→Run a backtest for the 'MovingAverageCross' strategy from 2023-01-01 to 2023-12-31.

→Perform a Monte Carlo simulation on the latest backtest results to assess risk.

→Optimize the hyperparameters for my current strategy to improve the Sharpe ratio.

→Generate a comprehensive risk report for the recent backtest session.

Original README from bkuri/jesse-mcp

Jesse MCP Server

An MCP (Model Context Protocol) server that exposes Jesse's algorithmic trading framework capabilities to LLM agents.

Status: Feature Complete ✅

All planned features implemented and tested. 32 tools available (17 core + 15 agent).

Installation

PyPI

pip install jesse-mcp

uvx (recommended for running directly)

uvx jesse-mcp

Arch Linux (AUR)

yay -S jesse-mcp
# or
paru -S jesse-mcp

From Source

git clone https://github.com/bkuri/jesse-mcp.git
cd jesse-mcp
pip install -e .

Usage

# stdio transport (default, for MCP clients)
jesse-mcp

# HTTP transport (for remote access)
jesse-mcp --transport http --port 8100

# Show help
jesse-mcp --help

Environment Variables

Variable	Description	Default
`JESSE_URL`	Jesse REST API URL	`http://server2:9100`
`JESSE_PASSWORD`	Jesse UI password	(required)
`JESSE_API_TOKEN`	Pre-generated API token	(alternative to password)

Features

Backtesting - Single and batch backtest execution via Jesse REST API
Optimization - Hyperparameter tuning with walk-forward validation
Monte Carlo Analysis - Statistical robustness testing
Pairs Trading - Cointegration testing and strategy generation
Strategy Management - CRUD operations for trading strategies
Risk Analysis - VaR, stress testing, comprehensive risk reports
Agent Tools - 15 specialized tools for autonomous trading workflows

Architecture

LLM Agent ←→ MCP Protocol ←→ jesse-mcp ←→ Jesse REST API (localhost:9000)
                                    ↓
                            Mock Fallbacks (when Jesse unavailable)

Available Tools (32 Total)

Core Tools (17)

Phase 1: Backtesting

Tool	Description
`backtest`	Run single backtest with specified parameters
`strategy_list`	List available strategies
`strategy_read`	Read strategy source code
`strategy_validate`	Validate strategy code

Phase 2: Data & Analysis

Tool	Description
`candles_import`	Download candle data from exchanges
`backtest_batch`	Run concurrent multi-asset backtests
`analyze_results`	Extract insights from backtest results
`walk_forward`	Walk-forward analysis for overfitting detection

Phase 3: Optimization

Tool	Description
`optimize`	Optimize hyperparameters using Optuna

Phase 4: Risk Analysis

Tool	Description
`monte_carlo`	Monte Carlo simulations for risk analysis
`var_calculation`	Value at Risk (historical, parametric, Monte Carlo)
`stress_test`	Test under extreme market scenarios
`risk_report`	Comprehensive risk assessment

Phase 5: Pairs Trading

Tool	Description
`correlation_matrix`	Cross-asset correlation analysis
`pairs_backtest`	Backtest pairs trading strategies
`factor_analysis`	Decompose returns into systematic factors
`regime_detector`	Identify market regimes and transitions

Agent Tools (15)

Specialized tools for autonomous trading workflows:

Tool	Description
`strategy_suggest_improvements`	AI-powered strategy enhancement suggestions
`strategy_compare_strategies`	Compare multiple strategies side-by-side
`strategy_optimize_pair_selection`	Optimize pairs trading selection
`strategy_analyze_optimization_impact`	Analyze impact of optimization changes
`risk_analyze_portfolio`	Portfolio-level risk analysis
`risk_stress_test`	Advanced stress testing
`risk_assess_leverage`	Leverage risk assessment
`risk_recommend_hedges`	Hedging recommendations
`risk_analyze_drawdown_recovery`	Drawdown recovery analysis
`backtest_comprehensive`	Full backtest with all metrics
`backtest_compare_timeframes`	Compare performance across timeframes
`backtest_optimize_parameters`	Quick parameter optimization
`backtest_monte_carlo`	Backtest with Monte Carlo analysis
`backtest_analyze_regimes`	Regime-aware backtest analysis
`backtest_validate_significance`	Statistical significance validation

Testing

# Install dev dependencies
pip install jesse-mcp[dev]

# Run all tests
pytest -v

# Run with coverage
pytest --cov=jesse_mcp

Status: 49 tests passing

Local Development

Prerequisites

Python 3.10+
Jesse 1.13.x running on localhost:9000
PostgreSQL on localhost:5432
Redis on localhost:6379

Start Jesse Stack (Podman)

Frequently Asked Questions

What are the key features of Jesse MCP Server?

Single and batch backtest execution via Jesse REST API. Hyperparameter tuning with walk-forward validation. Statistical robustness testing using Monte Carlo analysis. Comprehensive risk assessment including VaR and stress testing. 15 specialized agent tools for autonomous trading workflows.

What can I use Jesse MCP Server for?

Automating the backtesting of multiple trading strategies across different timeframes.. Performing hyperparameter optimization to find the most profitable strategy settings.. Conducting rigorous risk analysis and stress testing before deploying a strategy.. Analyzing market regimes and correlations to refine pairs trading strategies..

How do I install Jesse MCP Server?

Install Jesse MCP Server by running: pip install jesse-mcp

What MCP clients work with Jesse MCP Server?

Jesse MCP Server works with any MCP-compatible client including Claude Desktop, Claude Code, Cursor, and other editors with MCP support.

Conare · memory for coding agents

Turn this server into reusable context

Keep Jesse MCP Server docs, env vars, and workflow notes in Conare so your agent carries them across sessions.

Set up free$npx conare@latest

Add it to Claude Code

Make your agent remember this setup

What it does

Tools 5

Environment Variables

Try it

Jesse MCP Server

Status: Feature Complete ✅

Installation

PyPI

uvx (recommended for running directly)

Arch Linux (AUR)

From Source

Usage

Environment Variables

Features

Architecture

Available Tools (32 Total)

Core Tools (17)

Phase 1: Backtesting

Phase 2: Data & Analysis

Phase 3: Optimization

Phase 4: Risk Analysis

Phase 5: Pairs Trading

Agent Tools (15)

Testing

Local Development

Prerequisites

Start Jesse Stack (Podman)

Frequently Asked Questions

Turn this server into reusable context

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