jolt-mcp 0.5.1

Jolt MCP server unifies multiple LLMs for coding (Codex, Claude Code, Cursor, Gemini) through intelligent auto-discovery and standardized interface

Jolt MCP Server

Stop copy-pasting between AI models. Jolt MCP is a local MCP server that lets your primary AI assistant delegate tasks to specialized models like Gemini, Claude, Codex, and Cursor. Solve complex engineering problems in parallel, directly from your IDE.

Key Features:

• Context Continuity: Shared project context across all sub-agents
• Parallel Execution: All agents work simultaneously
• Model Specialization: Right AI for each task (Gemini's 1M context, Claude's reasoning, Codex's implementation)
• Zero Markup: Uses your existing CLI tools and API subscriptions
• 26+ IDE Support: Works with Claude Code, Cursor, VS Code, JetBrains, and more

Table of Contents

• Quick Start
• What is Jolt MCP
• Technical Architecture
• Why Multi-Agent vs Single AI
• Real-World Examples
• Installation
• IDE Integration
• Advanced Configuration
• Contributing
• License

Quick Start

# Install Jolt MCP
pip install jolt-mcp

# Check available AI tools
jolt-mcp --check

# Start with all available tools
jolt-mcp

# Use specific assistants only
jolt-mcp --agents codex,claude

One-liner for Claude Code:

claude mcp add jolt-mcp -- jolt-mcp --agents gemini,claude,codex,cursor

Try this multi-agent prompt in your IDE:

The user dashboard is randomly slow for enterprise customers.

Use Gemini SubAgent to analyze frontend performance issues in the React components, especially expensive re-renders and inefficient data fetching.

Use Codex SubAgent to examine the backend API endpoint for N+1 queries and database bottlenecks.

Use Claude SubAgent to review the infrastructure logs and identify memory/CPU pressure during peak hours.

What is Jolt MCP

Jolt MCP is a local Model Context Protocol (MCP) server that coordinates specialized AI sub-agents to solve complex engineering problems. Instead of manually switching between different AI tools, you delegate tasks from a single prompt in your IDE, and Jolt manages the coordination, context sharing, and response synthesis.

Key Benefits

• Context Continuity: The primary agent provides shared, rich context to all sub-agents
• Parallel Execution: All agents work simultaneously, drastically reducing wait time
• Model Specialization: Use the right AI for each task - Gemini's 1M context for analysis, Claude's reasoning for logic, Codex for implementation
• No Extra Cost: Uses your existing CLI tools and API subscriptions with zero markup
• Single Interface: One prompt, multiple specialized responses, automatically synthesized

Technical Architecture

    +----------------------------------+
    | Your IDE (VS Code, Cursor, etc.) |
    | (Primary AI Assistant)           |
    +----------------+-----------------+
                     |
    (1. User prompt with subagent delegation)
                     |
    +----------------v-----------------+
    |       Jolt MCP Server            |
    |         (localhost)              |
    +----------------+-----------------+
                     |
    (2. Dispatches tasks to sub-agent CLIs in parallel)
                     |
+--------------------v--------------------+
|                                         |
|  +-----------+   +-----------+   +-----------+  |
|  |  Gemini   |   |  Claude   |   |   Codex   |  |
|  | (Analysis)|   |  (Logic)  |   | (Implement)| |
|  +-----------+   +-----------+   +-----------+  |
|                                         |
+--------------------^--------------------+
                     |
    (3. Sub-agents execute using local tools,
        e.g., read_file, run_shell_command)
                     |
    +----------------+-----------------+
    |      Jolt MCP Server             |
    | (Aggregates & Synthesizes)       |
    +----------------+-----------------+
                     |
(4. Returns a single, synthesized response)
                     |
    +----------------v-----------------+
    | Your IDE (Primary AI Assistant)  |
    +----------------------------------+

How It Works

1. Context Continuity: The initial prompt and relevant file/project context are packaged by the primary agent. The MCP server passes this "context bundle" to each sub-agent, ensuring all participants have the same ground truth without manual copy-pasting.

2. Model Specialization: Use the right model for the job. Leverage Gemini's 1M context for codebase analysis, Claude's reasoning for logic and implementation, and Codex's proficiency for code generation and reviews, all in one workflow.

3. No Extra Cost: Jolt invokes the CLI tools you already have installed and configured. It uses your existing API keys and subscriptions. We add no markup. The cost is exactly what you would pay running the tools manually.

Why Multi-Agent vs Single AI

Because manual context-switching is slow, error-prone, and prevents deep analysis.

The Multi-Tab Workflow ❌

• Manually copy-paste code and context between different AI chats
• Each agent starts fresh, unaware of other conversations or files
• You wait for one agent to finish before starting the next
• You are responsible for merging disparate, often conflicting, advice
• High risk of pasting outdated code or incorrect context

The Jolt Workflow ✅

• Delegate tasks from a single prompt in your IDE
• The primary agent provides shared, rich context to all sub-agents
• All agents work in parallel, drastically reducing wait time
• The final output automatically synthesizes the best insights from each model
• The entire workflow is a single, deterministic, and repeatable command

Real-World Examples

Each example includes real code, logs, and explicit delegation to specialized sub-agents. Copy the whole block and paste it into your IDE assistant.

1. Multi-Stack Debugging — Virtual War Room for Production Issues

I'm debugging a critical production issue. The user sees a "Failed to load data" message.

Here is the browser console output:
```json
{
  "timestamp": "2024-09-24T10:05:21.123Z",
  "level": "error",
  "message": "API request failed for /api/v1/user/profile",
  "error": {
    "status": 500,
    "statusText": "Internal Server Error"
  }
}

Here is the backend server log:

ERROR: Exception in ASGI application
File "/app/services/user_service.py", line 42, in get_user_profile
  user_data = await db.fetch_one(query)
ValueError: Database connection is not available

Use Gemini SubAgent to analyze the logs from both stacks, correlate the events, and form a hypothesis about the root cause. Use Codex SubAgent to analyze the Python backend traceback and suggest a specific code fix for the database connection error. Use Claude SubAgent to review the frontend error handling and recommend more resilient patterns. Use Cursor SubAgent to search the codebase for other files that might have similar database connection issues.

At the end, aggregate all findings into a single incident report with root cause analysis and prioritized fixes.

2) Performance Optimization — API Latency & Database Query Tuning

```markdown
Our checkout API p95 latency increased from 220ms to 780ms. Need optimization strategy.

PostgreSQL slow query log:
```sql
-- Duration: 2455.112 ms
SELECT c.name, COUNT(o.id) AS total_orders, SUM(p.amount) AS revenue
FROM companies c, orders o, payments p
WHERE c.id = o.company_id
  AND o.id = p.order_id
  AND c.region = 'North America'
GROUP BY c.name
ORDER BY revenue DESC;

EXPLAIN ANALYZE shows:

Seq Scan on orders (cost=0.00..52000.00 rows=100000)
  Filter: (status = 'completed')
  Rows Removed by Filter: 134,201

Node.js hotspot from profiling:

// 40% CPU time
orders.map(o => ({ ...o, json: JSON.stringify(o) }));

// N+1 query problem
for (const id of orderIds) {
  await fetchInventory(id);
}

Use Claude SubAgent to analyze the EXPLAIN plan and identify why the query is slow. Use Codex SubAgent to rewrite the SQL with proper JOINs and suggest indexes. Use Gemini SubAgent to fix the N+1 query problem with batch fetching. Use Cursor SubAgent to find all instances of JSON.stringify in hot code paths.

Aggregate findings into a performance optimization plan with measurable improvements.

## Installation

### Using pip (Standard)

```bash
pip install jolt-mcp

Using UV/UVX (Recommended for faster installs)

uvx jolt-mcp@latest

IDE Integration

Jolt MCP supports 26+ MCP-compatible clients. Here are the top 7:

1. Claude Code

Using pip:

claude mcp add jolt-mcp -- jolt-mcp --agents gemini,claude,codex,cursor

Using UVX:

claude mcp add jolt-mcp -- uvx jolt-mcp@latest --agents gemini,claude,codex,cursor

2. Cursor

One-Click Install:

Or use this direct link:

cursor://anysphere.cursor-deeplink/mcp/install?name=jolt-mcp&config=eyJ0eXBlIjoic3RkaW8iLCJjb21tYW5kIjoidXZ4IiwiYXJncyI6WyJyb3VuZHRhYmxlLWFpQGxhdGVzdCJdLCJlbnYiOnsiQ0xJX01DUF9TVUJBR0VOVFMiOiJjb2RleCxjbGF1ZGUsY3Vyc29yLGdlbWluaSJ9fQo=

Manual Installation:

File: .cursor/mcp.json

Using pip:

{
  "mcpServers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

Using UVX:

{
  "mcpServers": {
    "jolt-mcp": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "jolt-mcp@latest"
      ],
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

3. Claude Desktop

File: ~/.config/claude_desktop_config.json

Using pip:

{
  "mcpServers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

Using UVX:

{
  "mcpServers": {
    "jolt-mcp": {
      "command": "uvx",
      "args": ["jolt-mcp@latest"],
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

4. VS Code

Add to settings.json:

Using pip:

{
  "mcp.servers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

Using UVX:

{
  "mcp.servers": {
    "jolt-mcp": {
      "command": "uvx",
      "args": ["jolt-mcp@latest"],
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

5. OpenAI Codex

File: ~/.codex/config.toml

Using pip:

# IMPORTANT: the top-level key is 'mcp_servers' rather than 'mcpServers'.
[mcp_servers.jolt-mcp]
command = "jolt-mcp"
args = []
env = { "CLI_MCP_SUBAGENTS" = "codex,claude,cursor,gemini" }

Using UVX:

# IMPORTANT: the top-level key is 'mcp_servers' rather than 'mcpServers'.
[mcp_servers.jolt-mcp]
command = "uvx"
args = ["jolt-mcp@latest"]
env = { "CLI_MCP_SUBAGENTS" = "codex,claude,cursor,gemini" }

6. Windsurf

File: ~/.codeium/windsurf/mcp_config.json

Using pip:

{
  "mcpServers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

Using UVX:

{
  "mcpServers": {
    "jolt-mcp": {
      "command": "uvx",
      "args": ["jolt-mcp@latest"],
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

7. Gemini CLI

File: ~/.gemini/settings.json

Using pip:

{
  "mcpServers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

Using UVX:

{
  "mcpServers": {
    "jolt-mcp": {
      "command": "uvx",
      "args": ["jolt-mcp@latest"],
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

Additional IDE Support

Jolt MCP integrates with 26+ different IDEs and AI coding tools:

JetBrains IDEs (IntelliJ, PyCharm, WebStorm, etc.)

Settings Path: Settings > Tools > AI Assistant > Model Context Protocol (MCP)

{
  "name": "jolt-mcp",
  "command": "jolt-mcp",
  "transport": "stdio",
  "env": {
    "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
  }
}

GitHub Copilot

{
  "github.copilot.mcp.servers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

---

🖥️ Desktop IDEs

JetBrains AI Assistant - IntelliJ, PyCharm, WebStorm, etc.

1. Settings Path: Settings > Tools > AI Assistant > Model Context Protocol (MCP)
2. Add New Server: Click "+" to add new MCP server
3. Configuration:

   {
     "name": "jolt-mcp",
     "command": "jolt-mcp",
     "transport": "stdio",
     "env": {
       "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini",
     }
   }
   

4. Apply & Restart: Apply settings and restart the IDE

Visual Studio 2022 - Microsoft's Flagship IDE

Create mcp_config.json in your project root:

{
  "servers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "transport": "stdio",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

Alternative: Use Extensions > Manage Extensions > Search for "Jolt MCP"

Zed - High-Performance Code Editor

Add to settings.json (Cmd/Ctrl + ,):

{
  "context_servers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

Extension Alternative: Search for "Jolt MCP" in Zed Extensions

---

💻 CLI Tools

Gemini CLI - Google's Gemini Command-Line Interface

Edit ~/.gemini/settings.json:

{
  "mcpServers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

Rovo Dev CLI - Atlassian's Development CLI

Configure via rovo config command:

# Add MCP server
rovo mcp add jolt-mcp jolt-mcp

# Verify
rovo mcp list

Manual configuration in ~/.rovo/config.json:

{
  "mcpServers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

Amazon Q Developer CLI - Amazon's AI Development Assistant

Edit configuration in ~/.aws/q-developer/config.json:

{
  "mcpServers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

Crush - Terminal-Based AI Assistant

Create or edit crush.json in your project:

{
  "mcp": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "transport": "stdio",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

Warp - AI-Powered Terminal

Configure via Warp settings:

1. Open Settings: Cmd/Ctrl + ,
2. Navigate to: Features > AI > MCP Servers
3. Add Server:

   {
     "name": "jolt-mcp",
     "command": "jolt-mcp",
     "env": {
       "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
     }
   }
   

---

🤖 AI Assistants

Claude Desktop - Anthropic's Desktop Application

Edit ~/.config/claude_desktop_config.json:

{
  "mcpServers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

Cline - AI Assistant Extension

One-Click Install:

1. Open Cline MCP Server Marketplace
2. Search for "Jolt MCP"
3. Click "Install"

Manual Configuration in cline_mcp_settings.json:

{
  "mcpServers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

BoltAI - AI Assistant Application

1. Open BoltAI Settings
2. Navigate to: Plugins > MCP Servers
3. Add New Server:
   • Name: jolt-mcp
   • Command: jolt-mcp
   • Environment Variables:

     CLI_MCP_SUBAGENTS=codex,claude,cursor,gemini
     

Perplexity Desktop - AI Search and Research Assistant

Configure in Perplexity settings:

{
  "mcpConfig": {
    "servers": {
      "jolt-mcp": {
        "command": "jolt-mcp",
        "env": {
          "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
        }
      }
    }
  }
}

Qodo Gen - AI Code Generation and Analysis Tool

Add to Qodo Gen configuration:

{
  "mcp_servers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

---

🛠️ Specialized Tools

Opencode - Open-Source AI Code Editor

Add to opencode_config.json:

{
  "mcpServers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

OpenAI Codex - OpenAI's Code Generation Model Interface

Edit config.toml:

[mcp_servers.jolt-mcp]
command = "jolt-mcp"
env = { CLI_MCP_SUBAGENTS = "codex,claude,cursor,gemini" }

Kiro - AI Development Assistant

Configure in ~/.kiro/config.json:

{
  "mcpServers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

Trae - AI Development Environment

Add to Trae workspace configuration:

{
  "mcp": {
    "servers": {
      "jolt-mcp": {
        "command": "jolt-mcp",
        "env": {
          "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
        }
      }
    }
  }
}

LM Studio - Local Language Model Interface

One-Click Install:

1. Navigate to Program > Install > Edit mcp.json
2. Search for "Jolt MCP" in marketplace
3. Click "Install"

Manual Configuration:

{
  "mcpServers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

Zencoder - AI-Powered Coding Assistant

Configure via Zencoder settings panel:

{
  "mcp_configuration": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

Augment Code - AI-Powered Code Completion

Add to Augment Code workspace settings:

{
  "mcpServers": {
    "jolt-mcp": {
      "command": "jolt-mcp",
      "transport": "stdio",
      "env": {
        "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
      }
    }
  }
}

Roo Code - AI Development Environment

Configure in Roo Code project settings:

{
  "ai_assistants": {
    "mcp_servers": {
      "jolt-mcp": {
        "command": "jolt-mcp",
        "env": {
          "CLI_MCP_SUBAGENTS": "codex,claude,cursor,gemini"
        }
      }
    }
  }
}

---

🔧 Configuration Tips

Environment Variables Reference

# Specify which AI assistants to enable
CLI_MCP_SUBAGENTS="codex,claude,cursor,gemini"


# Enable debug logging
CLI_MCP_DEBUG=true

# Override availability checking
CLI_MCP_IGNORE_AVAILABILITY=true

Command Alternatives

All IDEs support these equivalent commands:

• jolt-mcp (primary command)
• jolt-mcp-server (descriptive alias)
• python -m jolt_mcp_server (Python module)
• npx @joltpm/mcp (NPM package - coming soon)

Verification

After installation, verify the integration works:

# Check server availability
jolt-mcp --check

# Test connection (varies by IDE)
# Most IDEs will show "Jolt MCP" in their AI assistant panel

Troubleshooting

1. Server not found: Ensure jolt-mcp is in your PATH
2. Permission denied: Run chmod +x $(which jolt-mcp)
3. Config not loaded: Check file paths and JSON syntax
4. No AI tools detected: Run jolt-mcp --check first

Available MCP Tools

Once integrated, you get access to:

Availability Checks

• check_codex_availability - Verify Codex CLI status
• check_claude_availability - Verify Claude Code CLI status
• check_cursor_availability - Verify Cursor CLI status
• check_gemini_availability - Verify Gemini CLI status

Unified Task Execution

• execute_codex_task - Run coding tasks through Codex
• execute_claude_task - Run coding tasks through Claude Code
• execute_cursor_task - Run coding tasks through Cursor
• execute_gemini_task - Run coding tasks through Gemini

Advanced Configuration

Environment Variables

# Specify which assistants to enable
export CLI_MCP_SUBAGENTS="codex,gemini"


# Enable all tools regardless of availability
export CLI_MCP_IGNORE_AVAILABILITY=true

# Enable debug logging
export CLI_MCP_DEBUG=true

Command Line Options

jolt-mcp --help

Options:
  --agents TEXT     Comma-separated list of agents (gemini,claude,codex,cursor)
  --check          Check availability of all AI tools
  --debug          Enable debug logging
  --version        Show version information
  --help           Show this message and exit

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

---

Built for developers who value their time. Stop context-switching between AI tools and start solving problems faster with coordinated multi-agent workflows.

For more examples, advanced usage patterns, and troubleshooting guides, visit our GitHub repository.