Sub-Agents MCP Server

Bring Claude Code–style sub-agents to any MCP-compatible tool.

This MCP server lets you define task-specific AI agents (like "test-writer" or "code-reviewer") in markdown files, and execute them via Cursor CLI, Claude Code, Gemini CLI, or Codex backends.

Why?

Claude Code offers powerful sub-agent workflows—but they're limited to its own environment. This MCP server makes that workflow portable, so any MCP-compatible tool (Cursor, Claude Desktop, Windsurf, etc.) can use the same agents.

Concrete benefits:

• Define reusable agents once, use them across multiple tools

• Share agent definitions within teams regardless of IDE choice

• Leverage Cursor CLI, Claude Code, Gemini CLI, or Codex capabilities from any MCP client

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Table of Contents

• Prerequisites

• Quick Start

• Usage Examples

• Writing Effective Agents

• Agent Examples

• Configuration Reference

• Session Management

• Troubleshooting

• Design Philosophy

• How It Works

Prerequisites

• Node.js 20 or higher

• One of these execution engines (they actually run the sub-agents):

  • cursor-agent CLI (from Cursor)

  • claude CLI (from Claude Code)

  • gemini CLI (from Gemini CLI)

  • codex CLI (from Codex)

• An MCP-compatible tool (Cursor IDE, Claude Desktop, Windsurf, etc.)

Quick Start

1. Create Your First Agent

Create a folder for your agents and add code-reviewer.md:

See Writing Effective Agents for more on agent design.

2. Install Your Execution Engine

Pick one based on which tool you use:

For Cursor users:

For Claude Code users:

Note: Claude Code installs the claude CLI command.

For Gemini CLI users:

Note: Gemini CLI uses OAuth authentication. Run gemini once to authenticate via browser.

For Codex users:

3. Configure MCP

Add this to your MCP configuration file:

Cursor: ~/.cursor/mcp.json Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS)

Important: Use absolute paths only.

• ✅ /Users/john/Documents/my-agents (Mac/Linux)

• ✅ C:\\Users\\john\\Documents\\my-agents (Windows)

• ❌ ./agents or ~/agents won't work

Restart your IDE and you're ready to go.

4. Fix "Permission Denied" Errors When Running Shell Commands

Sub-agents may fail to execute shell commands with permission errors. This happens because sub-agents can't respond to interactive permission prompts.

Recommended approach:

1. Run your CLI tool directly with the task you want sub-agents to handle:

   # For Cursor users cursor-agent # For Claude Code users claude # For Gemini CLI users gemini # For Codex CLI users codex

2. When prompted to allow commands (e.g., "Add Shell(cd), Shell(make) to allowlist?"), approve them

3. This automatically updates your configuration file, and those commands will now work when invoked via MCP sub-agents

Manual configuration (alternative):

If you prefer to configure permissions manually, edit:

• Cursor: <project>/.cursor/cli.json or ~/.cursor/cli-config.json

• Claude Code: .claude/settings.json or .claude/settings.local.json

Note: Agents often run commands as one-liners like cd /path && make build, so you need to allow all parts of the command.

Usage Examples

Just tell your AI to use an agent:

Your AI automatically invokes the specialized agent and returns results.

Tip: Always include what you want done in your request—not just which agent to use. For example:

• ✅ "Use the code-reviewer agent to check my UserService class"

• ❌ "Use the code-reviewer agent" (too vague—the agent won't know what to review)

The more specific your task, the better the results.

Writing Effective Agents

The Single Responsibility Principle

Each agent should do one thing well. Avoid "swiss army knife" agents.

Essential Structure

Keep Agents Self-Contained

Agents run in isolation with fresh context. Avoid:

• References to other agents ("then use X agent...")

• Assumptions about prior context ("continuing from before...")

• Scope creep beyond the stated purpose

Advanced Patterns

For complex agents, consider adding:

• Scope boundaries: Explicitly state what's out of scope

• Prohibited actions: List common mistakes the agent should avoid

• Output format: Define structured output when needed

Agent Examples

Each .md or .txt file in your agents folder becomes an agent. The filename becomes the agent name (e.g., bug-investigator.md → "bug-investigator").

bug-investigator.md

For more advanced patterns (completion checklists, prohibited actions, structured output), see claude-code-workflows/agents. These are written for Claude Code, but the design patterns apply to any execution engine.

Configuration Reference

Required Environment Variables

AGENTS_DIR Path to your agents folder. Must be absolute.

AGENT_TYPE Which execution engine to use:

• "cursor" - uses cursor-agent CLI

• "claude" - uses claude CLI

• "gemini" - uses gemini CLI

• "codex" - uses codex CLI (OpenAI Codex)

Optional Settings

EXECUTION_TIMEOUT_MS How long agents can run before timing out (default: 5 minutes, max: 10 minutes)

AGENTS_SETTINGS_PATH Path to custom CLI settings directory for sub-agents.

Each CLI normally reads settings from project-level directories (.claude/, .cursor/, .codex/) or user-level directories (~/.claude/, ~/.cursor/, ~/.codex/). If you want sub-agents to run with different settings (e.g., different permissions or model), specify a separate settings directory here.

Supported CLI types: claude, cursor, codex

Note: Gemini CLI does not support custom settings paths, so this option has no effect when AGENT_TYPE is gemini.

Example with custom settings:

Security Note

Agents have access to your project directory. Only use agent definitions from trusted sources.

Session Management

Session management allows sub-agents to remember previous executions, which helps when you want agents to build on earlier work or maintain context across multiple calls.

Why Sessions Matter

By default, each sub-agent execution starts with no context. With sessions enabled:

• Agents can reference their earlier work

• You get execution history for debugging

• Related tasks share context

Enabling Sessions

Add these environment variables to your MCP configuration:

Configuration options:

• SESSION_ENABLED - Set to "true" to enable session management (default: false)

• SESSION_DIR - Where to store session files (default: .mcp-sessions in the current working directory)

• SESSION_RETENTION_DAYS - How long to keep session files based on last modification time in days (default: 1)

Security consideration: Session files contain execution history and may include sensitive information. Use absolute paths for SESSION_DIR.

When to Use Sessions

Sessions work well for:

• Iterative development: "Based on your earlier findings, now fix the issues"

• Multi-step workflows: Breaking complex tasks into smaller sub-agent calls

• Debugging: Reviewing exactly what was executed and what results were returned

Note that sessions require additional storage and processing overhead.

How Session Continuity Works

When sessions are enabled, the MCP response includes a session_id field. To continue the same session, pass this ID back in the next request.

Important: Your AI assistant must explicitly include the session_id in subsequent requests. While some assistants may do this automatically, it's not guaranteed. For reliable session continuity, add explicit instructions to your prompts or project rules.

Example prompt instruction:

Example project rule (e.g.,

Troubleshooting

Timeout errors or authentication failures

If using Cursor CLI: Run cursor-agent login to authenticate. Sessions can expire, so just run this command again if you see auth errors.

Verify installation:

If using Claude Code: Make sure the CLI is properly installed and accessible.

Agent not found

Check that:

• AGENTS_DIR points to the correct directory (use absolute path)

• Your agent file has .md or .txt extension

• The filename uses hyphens or underscores (no spaces)

Other execution errors

1. Verify AGENT_TYPE is set correctly (cursor, claude, gemini, or codex)

2. Ensure your chosen CLI tool is installed and accessible

3. Double-check that all environment variables are set in the MCP config

Recursive sub-agent calls (infinite loop)

If sub-agents keep spawning more sub-agents, there are typically two causes:

1. MCP configuration inheritance

Create a separate settings directory without the sub-agents MCP configuration and specify it via AGENTS_SETTINGS_PATH. This prevents sub-agents from having access to this MCP server.

2. AGENTS.md instruction inheritance (Codex)

Codex concatenates AGENTS.md from CODEX_HOME and project root. If your project AGENTS.md has delegation instructions, sub-agents inherit them too.

Solution: Don't place AGENTS.md at the project root. Use separate directories:

• Run main Codex with CODEX_HOME=/your-project/.codex-main

• Set AGENTS_SETTINGS_PATH=/your-project/.codex-sub in sub-agents-mcp config

Design Philosophy

Why Independent Contexts Matter

Every sub-agent starts with a fresh context. This adds some startup overhead for each call, but it ensures that every task runs independently and without leftover state from previous runs.

Context Isolation

• Each agent only receives the information relevant to its task

• No context leakage between runs

• The main agent stays focused and lightweight

Accuracy and Reliability

• Sub-agents can specialize in a single goal without interference

• Less risk of confusion from unrelated context

• More consistent results in complex, multi-step workflows

Scalability

• Large tasks can be safely split into smaller sub-tasks

• Each sub-agent operates within its own token limit

• The main agent coordinates without hitting global context limits

The startup overhead is an intentional trade-off: the system favors clarity and accuracy over raw execution speed.

How It Works

This MCP server acts as a bridge between your AI tool and a supported execution engine (Cursor CLI, Claude Code, Gemini CLI, or Codex).

The flow:

1. You configure the MCP server in your client (Cursor, Claude Desktop, etc.)

2. The client automatically launches sub-agents-mcp as a background process when it starts

3. When your main AI assistant needs a sub-agent, it makes an MCP tool call

4. The MCP server reads the agent definition (markdown file) and invokes the selected CLI (cursor-agent, claude, gemini, or codex)

5. The execution engine runs the agent and streams results back through the MCP server

6. Your main assistant receives the results and continues working

This architecture lets any MCP-compatible tool benefit from specialized sub-agents, even if it doesn't have native support.

License

MIT

AI-to-AI collaboration through Model Context Protocol