spindle-mcp 1.0.1

MCP server for Claude Code to Claude Code delegation

Spindle

MCP server for Claude Code to Claude Code delegation. Spawn background agents that run asynchronously, with optional git worktree isolation for safe parallel work.

Features

• Async agent spawning - Fire-and-forget pattern with spool IDs
• Optional blocking with gather/yield - Wait for all results at once, or stream them as agents complete. Alternatively, agent can continue other work, spins are nonblocking by default
• Permission profiles - Control what tools child agents can use (readonly, careful, full)
• Shard isolation - Run agents in sandboxed git worktrees to prevent conflicts
• Model selection - Route tasks to haiku, sonnet, or opus per-agent
• Session continuity - Resume conversations with child agents (auto-recovers expired sessions)
• Rich querying - Search, filter, peek at running output, export results

Requirements

• Python 3.10+
• Claude CLI installed and authenticated
• Git (for shard/worktree functionality)

Install

pip install spindle-mcp

Add to Claude Code's MCP config (~/.claude.json):

{
  "mcpServers": {
    "spindle": {
      "command": "spindle"
    }
  }
}

Usage

Basic: Spawn and collect

# Spawn an agent
spool_id = spin("Research the Python GIL")

# Do other work...

# Check result
result = unspool(spool_id)

Permission profiles

Control what tools the spawned agent can use:

# Read-only: Can only search and read
spin("Analyze the codebase", permission="readonly")

# Careful (default): Can read/write but limited bash
spin("Fix this bug", permission="careful")

# Full access: No restrictions
spin("Implement the feature", permission="full")

# Shard: Full access + auto-isolated worktree (common for risky work)
spin("Refactor the auth system", permission="shard")

# Careful + shard: Limited tools but isolated
spin("Update configs", permission="careful+shard")

Profiles:

• readonly: Read, Grep, Glob, safe bash (ls, cat, git status/log/diff)
• careful: Read, Write, Edit, Grep, Glob, bash for git/make/pytest/python/npm
• full: No restrictions
• shard: Full access + auto-creates isolated worktree
• careful+shard: Careful permissions + auto-creates isolated worktree

You can also pass explicit allowed_tools to override the profile.

Isolated workspaces with shards

Run agents in isolated git worktrees to prevent conflicts:

# Agent works in its own worktree
spool_id = spin("Refactor auth module", shard=True)

# Check shard status
shard_status(spool_id)

# Merge changes back when done
shard_merge(spool_id)

# Or discard if not needed
shard_abandon(spool_id)

Shards create a git worktree + branch. If SKEIN is available, uses skein shard spawn for richer tracking. Falls back to plain git worktree otherwise.

Wait for completion

# Spawn multiple agents
id1 = spin("Find all TODO comments")
id2 = spin("List unused imports")
id3 = spin("Check for type errors")

# Gather: block until all complete, get all results
results = spin_wait("id1,id2,id3", mode="gather")

# Yield: return as each completes
# Great when results are independent - process each as it lands
result = spin_wait("id1,id2,id3", mode="yield")  # Returns first to finish

# With timeout
results = spin_wait("id1,id2", mode="gather", timeout=300)

Yield mode keeps you responsive instead of blocking on the slowest agent.

Model selection and timeouts

# Route quick tasks to haiku (fast, cheap)
spin("Summarize this file", model="haiku")

# Complex work to opus
spin("Design the new architecture", model="opus")

# Auto-kill if it takes too long
spin("Should be quick", timeout=60)

Continue a session

# Get session ID from completed spool
result = unspool(spool_id)  # includes session_id

# Continue that conversation
new_id = respin(session_id, "Follow up question")

If the session has expired on Claude's end, respin automatically falls back to transcript injection to recreate context.

Cancel running work

spin_drop(spool_id)

List all spools

spools()

Search and filter

# Search prompts and results
spool_search("authentication")

# Filter by status and time
spool_results(status="error", since="1h")

# Regex search results
spool_grep("error|failed|exception")

# Get statistics
spool_stats()

# Export to file
spool_export("all", format="md")

API

Tool	Purpose
spin(prompt, permission?, shard?, system_prompt?, working_dir?, allowed_tools?, tags?)	Spawn agent, return spool_id
unspool(spool_id)	Get result (non-blocking)
spools()	List all spools
spin_wait(spool_ids, mode?, timeout?)	Block until done
respin(session_id, prompt)	Continue session
spin_drop(spool_id)	Cancel by killing process
spool_search(query, field?)	Search prompts/results
spool_results(status?, since?, limit?)	Bulk fetch with filters
spool_grep(pattern)	Regex search results
spool_retry(spool_id)	Re-run with same params
spool_peek(spool_id, lines?)	See partial output while running
spool_dashboard()	Overview of running/complete/needs-attention
spool_stats()	Get summary statistics
spool_export(spool_ids, format?, output_path?)	Export to file
shard_status(spool_id)	Check shard worktree status
shard_merge(spool_id, keep_branch?)	Merge shard to master
shard_abandon(spool_id, keep_branch?)	Discard shard

Storage

Spools persist to ~/.spindle/spools/{spool_id}.json:

{
  "id": "abc12345",
  "status": "complete",
  "prompt": "...",
  "result": "...",
  "session_id": "...",
  "permission": "careful",
  "allowed_tools": "...",
  "tags": ["batch-1"],
  "shard": {
    "worktree_path": "/path/to/worktrees/abc12345-...",
    "branch_name": "shard-abc12345-...",
    "shard_id": "..."
  },
  "pid": 12345,
  "created_at": "2025-11-26T...",
  "completed_at": "2025-11-26T..."
}

CLI Commands

spindle start   # Start via systemd (or background if no service)
spindle reload  # Restart via systemd to pick up code changes
spindle status  # Check if running (hits /health endpoint)
spindle serve --http  # Run MCP server directly (what systemd calls)

systemd Service

For production, use the systemd service:

# Install service (copy to ~/.config/systemd/user/spindle.service)
systemctl --user daemon-reload
systemctl --user enable spindle
systemctl --user start spindle

Then spindle reload works to restart after code changes.

Hot Reload (MCP tool)

From within Claude Code, call spindle_reload() to restart the server and pick up code changes.

Configuration

Environment variables:

Variable	Default	Description
SPINDLE_MAX_CONCURRENT	15	Maximum concurrent spools

Storage location: ~/.spindle/spools/

How It Works

1. spin() spawns a detached claude CLI process with the given prompt
2. The process runs in background, writing output to temporary files
3. A monitor thread polls for completion
4. unspool() returns the result once complete (non-blocking check)
5. Spool metadata persists to JSON files, surviving server restarts

For shards:

1. A git worktree is created with a new branch
2. The agent runs inside that worktree
3. After completion, merge back with shard_merge() or discard with shard_abandon()

Limits

• Max 15 concurrent spools (configurable via SPINDLE_MAX_CONCURRENT)
• 24h auto-cleanup of old spools
• Orphaned spools (dead process) marked as error on restart

Contributing

See CONTRIBUTING.md for development setup and guidelines.

License

MIT - see LICENSE.