# Sub-Agents MCP Server
[](https://www.npmjs.com/package/sub-agents-mcp)
[](https://opensource.org/licenses/MIT)
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
→ [Read the full story](https://dev.to/shinpr/bringing-claude-codes-sub-agents-to-any-mcp-compatible-tool-1hb9)
## Table of Contents
- [Prerequisites](#prerequisites)
- [Quick Start](#quick-start)
- [Usage Examples](#usage-examples)
- [Writing Effective Agents](#writing-effective-agents)
- [Agent Examples](#agent-examples)
- [Configuration Reference](#configuration-reference)
- [Session Management](#session-management)
- [Troubleshooting](#troubleshooting)
- [Design Philosophy](#design-philosophy)
- [How It Works](#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`:
```markdown
# Code Reviewer
Review code for quality and maintainability issues.
## Task
- Find bugs and potential issues
- Suggest improvements
- Check code style consistency
## Done When
- All target files reviewed
- Issues listed with explanations
```
See [Writing Effective Agents](#writing-effective-agents) for more on agent design.
### 2. Install Your Execution Engine
Pick one based on which tool you use:
**For Cursor users:**
```bash
# Install Cursor CLI (includes cursor-agent)
curl https://cursor.com/install -fsS | bash
# Authenticate (required before first use)
cursor-agent login
```
**For Claude Code users:**
```bash
# Option 1: Native install (recommended)
curl -fsSL https://claude.ai/install.sh | bash
# Option 2: NPM (requires Node.js 18+)
npm install -g @anthropic-ai/claude-code
```
Note: Claude Code installs the `claude` CLI command.
**For Gemini CLI users:**
```bash
# Install Gemini CLI
npm install -g @google/gemini-cli
# Authenticate via browser (required before first use)
gemini
```
Note: Gemini CLI uses OAuth authentication. Run `gemini` once to authenticate via browser.
**For Codex users:**
```bash
# Install Codex
npm install -g @openai/codex
```
### 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)
```json
{
"mcpServers": {
"sub-agents": {
"command": "npx",
"args": ["-y", "sub-agents-mcp"],
"env": {
"AGENTS_DIR": "/absolute/path/to/your/agents-folder",
"AGENT_TYPE": "cursor" // or "claude", "gemini", or "codex"
}
}
}
}
```
**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:
```bash
# 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`
```json
{
"permissions": {
"allow": [
"Shell(cd)",
"Shell(make)",
"Shell(git)"
]
}
}
```
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:
```
"Use the code-reviewer agent to check my UserService class"
```
```
"Use the test-writer agent to create unit tests for the auth module"
```
```
"Use the doc-writer agent to add JSDoc comments to all public methods"
```
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.
| ✅ Good | ❌ Bad |
|---------|--------|
| Reviews code for security issues | Reviews code, writes tests, and refactors |
| Writes unit tests for a module | Writes tests and fixes bugs it finds |
### Essential Structure
```markdown
# Agent Name
One-sentence purpose.
## Task
- Action 1
- Action 2
## Done When
- Criterion 1
- Criterion 2
```
### 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`**
```markdown
# Bug Investigator
Investigate bug reports and identify root causes.
## Task
- Collect evidence from error logs, code, and git history
- Generate multiple hypotheses for the cause
- Trace each hypothesis to its root cause
- Report findings with supporting evidence
## Out of Scope
- Fixing the bug (investigation only)
- Making assumptions without evidence
## Done When
- At least 2 hypotheses documented with evidence
- Most likely cause identified with confidence level
- Affected code locations listed
```
For more advanced patterns (completion checklists, prohibited actions, structured output), see [claude-code-workflows/agents](https://github.com/shinpr/claude-code-workflows/tree/main/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:
```json
{
"mcpServers": {
"sub-agents": {
"command": "npx",
"args": ["-y", "sub-agents-mcp"],
"env": {
"AGENTS_DIR": "/absolute/path/to/agents",
"AGENT_TYPE": "cursor",
"EXECUTION_TIMEOUT_MS": "600000",
"AGENTS_SETTINGS_PATH": "/absolute/path/to/custom-cli-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:
```json
{
"mcpServers": {
"sub-agents": {
"command": "npx",
"args": ["-y", "sub-agents-mcp"],
"env": {
"AGENTS_DIR": "/absolute/path/to/agents",
"AGENT_TYPE": "cursor",
"SESSION_ENABLED": "true",
"SESSION_DIR": "/absolute/path/to/session-storage",
"SESSION_RETENTION_DAYS": "1"
}
}
}
}
```
**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:**
```markdown
When using sub-agents with sessions enabled, always include the session_id
from the previous response in your next request to maintain context.
```
**Example project rule (e.g., `AGENTS.md`):**
```markdown
# Sub-Agent Session Guidelines
When calling the same sub-agent multiple times:
1. Extract the session_id from the MCP response
2. Pass it as a parameter in subsequent calls
3. This preserves context between executions
```
## 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:
```bash
which cursor-agent
```
**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:
```
/your-project
├── .codex-main/AGENTS.md # Main agent instructions
├── .codex-sub/AGENTS.md # Sub-agent instructions (no delegation)
└── (no AGENTS.md at root)
```
- 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
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*AI-to-AI collaboration through Model Context Protocol*
