Is settings safe to install?

SaferSkills independently audited settings (Hook) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.

CategoryWeightCategory scoreContribution

Securityprompt, exec, net, exfil, eval

35%

100

35.0 pts

Supply chainhash, typosquat, maintainer, lockfile

20%

100

20.0 pts

Maintenancestaleness, pinning, CI

15%

100

15.0 pts

TransparencySKILL.md, perms, README

15%

100

15.0 pts

Communityinstalls, verify, response

15%

100

15.0 pts

Findings & checks · 0 flagged

Securityscore 100 · 0 findings

✓—All security checks passedNo findings in this category for the latest scan.pass

Supply chainscore 100 · 0 findings

✓—All supply chain checks passedNo findings in this category for the latest scan.pass

Maintenancescore 100 · 0 findings

✓—All maintenance checks passedNo findings in this category for the latest scan.pass

Transparencyscore 100 · 0 findings

✓—All transparency checks passedNo findings in this category for the latest scan.pass

Communityscore 100 · 0 findings

✓—All community checks passedNo findings in this category for the latest scan.pass

The primary manifest — the file an agent reads to learn what this artifact does.

README.md · 1 file

README.md8.9 KB · Markdown

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)
`JESSE_TRADE_API_KEY`	jesse.trade community API Bearer token	(optional)

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
Community Browsing - Browse, compare, and inspect jesse.trade community strategies

Architecture

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

Available Tools

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

Community Tools (5)

Browse, compare, and inspect community strategies from jesse.trade:

Tool	Description
`list_periods`	List available backtest periods on jesse.trade
`browse_community_strategies`	Browse strategies sorted by performance metrics
`get_strategy_metrics`	Detailed backtest metrics for a specific strategy
`get_strategy_code`	Full Python source code for a strategy
`compare_community_strategies`	Side-by-side metric comparison of multiple strategies

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)

# Start infrastructure
podman run -d --name jesse-postgres --network host \
  -e POSTGRES_USER=jesse_user -e POSTGRES_PASSWORD=password -e POSTGRES_DB=jesse_db \
  docker.io/library/postgres:14-alpine

podman run -d --name jesse-redis --network host \
  docker.io/library/redis:6-alpine redis-server --save "" --appendonly no

# Start Jesse
podman run -d --name jesse --network host \
  -v /path/to/jesse-bot:/home:z \
  docker.io/salehmir/jesse:latest bash -c "cd /home && jesse run"

Start Dev MCP Server

./scripts/start-dev-server.sh   # Start on port 8100
./scripts/stop-dev-server.sh    # Stop server

Add to OpenCode

Add to ~/.config/opencode/opencode.json:

{
  "mcp": {
    "jesse-mcp-dev": {
      "type": "remote",
      "url": "http://localhost:8100/mcp",
      "enabled": true
    }
  }
}

Documentation

Using with LLMs - How to use with MCP-compatible LLMs
Production Deployment - Production deployment guide
Jesse Setup - Jesse integration setup
Agent System - Agent architecture
AGENTS.md - Development guidelines for AI agents

API Reference

Jesse REST Client

The jesse_rest_client.py module provides direct access to Jesse's REST API:

from jesse_mcp.core.jesse_rest_client import get_jesse_rest_client

client = get_jesse_rest_client()

# Run backtest
result = client.backtest(
    strategy="OctopusStrategy",
    symbol="BTC-USDT", 
    timeframe="1h",
    start_date="2024-01-01",
    end_date="2024-01-31"
)

Mock Implementations

When Jesse is unavailable, all tools gracefully fall back to mock implementations that return realistic synthetic data. This enables development and testing without a full Jesse installation.

Key Dependencies

Package	Version	Purpose
fastmcp	>=0.3.0	MCP server framework
numpy	>=1.24.0	Numerical computations
pandas	>=2.0.0	Data manipulation
scipy	>=1.10.0	Statistical functions
scikit-learn	>=1.3.0	ML utilities
optuna	>=3.0.0	Hyperparameter optimization

Project Structure

jesse_mcp/
├── server.py            # FastMCP server with 17 core tools
├── optimizer.py         # Phase 3: Optimization tools
├── risk_analyzer.py     # Phase 4: Risk analysis tools
├── pairs_analyzer.py    # Phase 5: Pairs trading tools
├── agent_tools.py       # 15 agent-specific tools
├── core/
│   ├── integrations.py  # Jesse framework integration
│   ├── jesse_rest_client.py  # REST API client
│   └── mock.py          # Mock implementations
├── agents/
│   ├── base.py          # Base agent class
│   ├── backtester.py    # Backtesting specialist
│   └── risk_manager.py  # Risk management specialist
└── scripts/
    ├── start-dev-server.sh
    └── stop-dev-server.sh

License

MIT License - see LICENSE file for details.

Publishing

This package uses GitHub Actions with PyPI trusted publishing. To release a new version:

Update version in pyproject.toml and jesse_mcp/__init__.py
Create a git tag: git tag v1.x.x
Push tag: git push origin v1.x.x
Create GitHub release - automatically publishes to PyPI

settings— hook

Is settings safe to install?

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

Core Tools (17)

Agent Tools (15)

Community Tools (5)

Testing

Local Development

Prerequisites

Start Jesse Stack (Podman)

Start Dev MCP Server

Add to OpenCode

Documentation

API Reference

Jesse REST Client

Mock Implementations

Key Dependencies

Project Structure

License

Publishing

Audit the pieces. Scan the whole. Decide.