Add enhanced onboarding examples and Xilope production guide
**Onboarding Enhancements**: - Added explicit server discovery examples - Included Claude Code server usage patterns - Added read_file tool call examples with proper namespacing **Xilope Production Guide**: - Complete production environment documentation - Memory storage integration details (/mnt/data/claude/claude/.mcp-memory/) - cmem wrapper integration (~/bin/cmem → /usr/local/bin/cmem) - Bidirectional sync workflows and best practices **Server Updates**: - Enhanced onboarding server to serve predefined markdown files - Priority: predefined files → default.md → fallback instructions This provides comprehensive guidance for both general MCP Browser usage and Xilope's specific production environment with cmem integration. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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@ -46,6 +46,17 @@ mcp_call(method="tools/call", params={"name": "brave_web_search", "arguments": {
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## Discovery Examples
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### Listing Available Servers
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```python
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# First, discover what MCP servers are available
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mcp_discover(jsonpath="$.servers[*].name")
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# Returns: ["builtin", "claude-code", "filesystem", etc.]
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# Get detailed info about servers
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mcp_discover(jsonpath="$.servers[*]")
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```
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### Basic Tool Discovery
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```python
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# Discover all available tools (built-in + external servers)
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mcp_discover(jsonpath="$.tools[*].name")
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@ -53,13 +64,20 @@ mcp_discover(jsonpath="$.tools[*].name")
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# Get tools from specific server
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mcp_discover(jsonpath="$.servers.brave-search.tools[*].name")
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# Get all configured servers
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mcp_discover(jsonpath="$.servers[*].name")
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# Get tool details
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mcp_discover(jsonpath="$.tools[?(@.name=='brave_web_search')]")
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```
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### Server-Specific Tool Discovery
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```python
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# Example: Get all Claude Code tools
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mcp_discover(jsonpath="$.servers['claude-code'].tools[*].name")
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# Returns: ["read_file", "write_file", "list_directory", etc.]
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# Get details of a specific tool from claude-code server
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mcp_discover(jsonpath="$.servers['claude-code'].tools[?(@.name=='read_file')]")
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```
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## Using External Server Tools
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Once discovered, call any tool through `mcp_call`:
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@ -82,6 +100,29 @@ mcp_call(
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"arguments": {"query": "mcp-browser"}
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}
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)
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# Example: Using Claude Code server to read a file
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mcp_call(
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method="tools/call",
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params={
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"name": "claude-code::read_file",
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"arguments": {
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"path": "/path/to/file.py",
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"start_line": 1,
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"end_line": 50
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}
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}
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)
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# Alternative syntax for server-namespaced tools
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mcp_call(
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method="tools/call",
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params={
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"name": "read_file",
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"arguments": {"path": "/path/to/file.py"},
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"server": "claude-code"
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}
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)
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```
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## Runtime Configuration
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@ -125,17 +125,21 @@ class OnboardingServer(BaseMCPServer):
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content = self._format_onboarding(identity, data)
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return self.content_text(content)
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else:
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# Try to load predefined markdown files first
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predefined_file = Path(__file__).parent / f"{identity}.md"
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if predefined_file.exists():
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with open(predefined_file) as f:
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predefined_content = f.read()
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return self.content_text(predefined_content)
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# Try to load default onboarding
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default_file = self.onboarding_dir / "default.md"
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default_file = Path(__file__).parent / "default.md"
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if default_file.exists():
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with open(default_file) as f:
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default_content = f.read()
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return self.content_text(
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f"# Onboarding for {identity}\n\n"
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f"No specific onboarding found. Using default:\n\n"
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f"{default_content}"
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)
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return self.content_text(default_content)
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else:
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return self.content_text(
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f"# Onboarding for {identity}\n\n"
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@ -0,0 +1,223 @@
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# Xilope Production Environment - MCP Browser Guide
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Welcome to Xilope's production MCP Browser setup! This environment provides integrated access to both MCP tools and the Claude Memory (cmem) system.
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## Production Architecture
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### Memory Storage Integration
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- **Local Storage**: `/mnt/data/claude/claude/.mcp-memory/`
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- **cmem Integration**: Bidirectional sync via `/mnt/data/claude/claude/bin/cmem`
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- **Identity-Based**: Each project gets separate memory space
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- **Persistent**: Memory survives across AI assistant sessions
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### Built-in Servers Available
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1. **Memory Server** (`builtin:memory::`):
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- `task_add`, `task_list`, `task_update` - Task management with cmem sync
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- `decision_add` - Decision tracking with reasoning
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- `pattern_add`, `pattern_resolve` - Learning pattern management
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- `knowledge_add`, `knowledge_get` - Fact storage and retrieval
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- `project_switch` - Switch between project contexts
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- `memory_summary` - Get overview of stored information
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2. **Screen Server** (`builtin:screen::`):
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- `create_session`, `execute`, `peek` - GNU screen management
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- `list_sessions`, `kill_session` - Session lifecycle
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- `enable_multiuser`, `attach_multiuser`, `add_user` - Collaboration
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3. **Pattern Server** (`builtin:patterns::`):
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- `add_pattern`, `list_patterns` - Auto-response pattern management
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- `test_pattern`, `execute_pattern` - Pattern execution
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4. **Onboarding Server** (`builtin:onboarding::`):
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- `onboarding` - Identity-aware instructions
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- `onboarding_list`, `onboarding_delete`, `onboarding_export` - Management
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## Production Workflows
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### Starting a Session
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```python
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# Check what servers are available
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mcp_discover(jsonpath="$.servers[*].name")
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# List all memory tools for task management
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mcp_discover(jsonpath="$.tools[?(@.name =~ /memory|task/)]")
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# Check current project context
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mcp_call(
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method="tools/call",
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params={
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"name": "builtin:memory::memory_summary",
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"arguments": {}
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}
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)
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```
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### Task Management with cmem Sync
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```python
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# Add a new task (automatically syncs to cmem)
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mcp_call(
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method="tools/call",
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params={
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"name": "builtin:memory::task_add",
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"arguments": {
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"content": "Implement feature X",
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"priority": "high",
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"assignee": "next-ai"
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}
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}
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)
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# List active tasks
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mcp_call(
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method="tools/call",
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params={
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"name": "builtin:memory::task_list",
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"arguments": {"status": "pending"}
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}
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)
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# Update task status (syncs completion to cmem)
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mcp_call(
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method="tools/call",
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params={
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"name": "builtin:memory::task_update",
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"arguments": {
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"task_id": "abc123",
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"status": "completed"
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}
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}
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)
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```
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### Decision and Pattern Management
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```python
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# Record important decisions (synced to cmem)
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mcp_call(
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method="tools/call",
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params={
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"name": "builtin:memory::decision_add",
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"arguments": {
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"choice": "Use Docker for deployment",
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"reasoning": "Simplifies environment management",
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"alternatives": ["Native install", "VM deployment"]
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}
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}
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)
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# Add learning patterns (synced to cmem)
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mcp_call(
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method="tools/call",
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params={
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"name": "builtin:memory::pattern_add",
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"arguments": {
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"pattern": "Always test before commit",
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"description": "Run full test suite before any git commit",
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"priority": "high"
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}
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}
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)
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```
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### Screen Session Management
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```python
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# Create a development session
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mcp_call(
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method="tools/call",
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params={
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"name": "builtin:screen::create_session",
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"arguments": {
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"session_name": "development",
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"working_directory": "/mnt/data/claude/claude"
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}
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}
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)
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# Execute commands in session
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mcp_call(
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method="tools/call",
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params={
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"name": "builtin:screen::execute",
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"arguments": {
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"session_name": "development",
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"command": "git status"
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}
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}
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)
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# Peek at session output
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mcp_call(
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method="tools/call",
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params={
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"name": "builtin:screen::peek",
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"arguments": {"session_name": "development"}
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}
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)
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```
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### Project Context Switching
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```python
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# Switch to different project memory space
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mcp_call(
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method="tools/call",
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params={
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"name": "builtin:memory::project_switch",
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"arguments": {"project_name": "mcp-browser"}
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}
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)
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# Use onboarding for project-specific instructions
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mcp_call(
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method="tools/call",
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params={
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"name": "builtin:onboarding::onboarding",
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"arguments": {
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"identity": "mcp-browser",
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"instructions": "Focus on context optimization and AI-first development"
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}
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}
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)
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```
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## cmem Integration Details
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### Memory Storage Structure
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```
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/mnt/data/claude/claude/.mcp-memory/
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├── default/ # Default project space
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│ ├── tasks.json # Task storage
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│ ├── decisions.json # Decision history
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│ ├── patterns.json # Learning patterns
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│ └── knowledge.json # Knowledge base
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├── mcp-browser/ # Project-specific space
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└── [other-projects]/ # Additional projects
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```
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### cmem Wrapper
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- **Location**: `/mnt/data/claude/claude/bin/cmem`
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- **Function**: Wraps `/usr/local/bin/cmem` with proper directory context
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- **Integration**: Automatic bidirectional sync with MCP memory server
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- **Commands**: `cmem handoff`, `cmem task add`, `cmem pattern add`, etc.
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### Sync Behavior
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- **Automatic**: All memory operations sync to cmem in background
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- **Graceful**: If cmem unavailable, operations continue locally
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- **Identity-Aware**: Each project gets separate cmem context
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- **Bidirectional**: Changes in either system propagate to the other
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## Production Best Practices
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1. **Always check memory summary** at start of session
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2. **Use task management** to track work across AI sessions
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3. **Record decisions** with reasoning for future reference
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4. **Create patterns** to capture effective approaches
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5. **Switch project contexts** when working on different codebases
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6. **Use screen sessions** for persistent development environments
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## Error Handling
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If cmem sync fails:
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- Operations continue with local storage
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- Sync retries automatically when cmem becomes available
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- No data loss occurs during temporary cmem unavailability
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This production setup ensures seamless AI assistant transitions while maintaining full project context and memory across sessions.
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