MCP Orchestration Skill

Central orchestration hub for PAL MCP and Rube MCP capabilities. Use this skill for complex workflows requiring multi-model reasoning, external service integration, or both.

Quick Start

PAL-powered analysis

/sc:mcp analyze --pal consensus --question "Should we use microservices?"

Rube-powered automation

/sc:mcp automate --rube --apps slack,github --workflow "notify on PR"

Combined orchestration

/sc:mcp orchestrate --pal thinkdeep --rube --full-validation

PAL MCP Integration

Available Tools

Tool Invocation Purpose

chat

mcp__pal__chat

Collaborative thinking, brainstorming

thinkdeep

mcp__pal__thinkdeep

Multi-stage investigation, complex analysis

planner

mcp__pal__planner

Sequential planning with branching

consensus

mcp__pal__consensus

Multi-model voting on decisions

codereview

mcp__pal__codereview

Systematic code quality analysis

precommit

mcp__pal__precommit

Git change validation

debug

mcp__pal__debug

Root cause analysis

challenge

mcp__pal__challenge

Force critical thinking

apilookup

mcp__pal__apilookup

Current API/SDK documentation

listmodels

mcp__pal__listmodels

Available AI models

clink

mcp__pal__clink

External CLI integration

PAL Workflows

Consensus Decision Making

Use consensus for:

• Architectural decisions (2-3 models)
• Security validations (security-focused models)
• Technology choices (diverse perspectives)
• Complex trade-off analysis

Recommended model combinations:

• Architectural: gpt-5.2 (for), gemini-3-pro (against), deepseek (neutral)

• Security: gpt-5.2 (security focus), gemini-3-pro (attack surface)

• Performance: gpt-5.2 (optimization), deepseek (efficiency)

Debug Investigation

Use debug for:

• Complex bugs with unclear causes
• Performance issues
• Race conditions
• Memory leaks
• Integration problems

Debug confidence levels: exploring -> low -> medium -> high -> very_high -> almost_certain -> certain

Code Review

Use codereview for:

• Pre-merge validation
• Security audits
• Performance reviews
• Architecture compliance

Review types: full, security, performance, quick

Rube MCP Integration

Available Tools

Tool Invocation Purpose

SEARCH_TOOLS

mcp__rube__RUBE_SEARCH_TOOLS

Discover available integrations

GET_RECIPE_DETAILS

mcp__rube__RUBE_GET_RECIPE_DETAILS

Get details of saved recipes

MULTI_EXECUTE

mcp__rube__RUBE_MULTI_EXECUTE_TOOL

Parallel tool execution

REMOTE_BASH

mcp__rube__RUBE_REMOTE_BASH_TOOL

Remote shell commands

REMOTE_WORKBENCH

mcp__rube__RUBE_REMOTE_WORKBENCH

Python sandbox execution

CREATE_RECIPE

mcp__rube__RUBE_CREATE_UPDATE_RECIPE

Save reusable workflows

EXECUTE_RECIPE

mcp__rube__RUBE_EXECUTE_RECIPE

Run saved recipes

FIND_RECIPE

mcp__rube__RUBE_FIND_RECIPE

Search existing recipes

MANAGE_CONNECTIONS

mcp__rube__RUBE_MANAGE_CONNECTIONS

App authentication

GET_SCHEMAS

mcp__rube__RUBE_GET_TOOL_SCHEMAS

Tool input schemas

MANAGE_SCHEDULE

mcp__rube__RUBE_MANAGE_RECIPE_SCHEDULE

Recipe scheduling

Rube Workflows

External Integration Flow

1. SEARCH_TOOLS - Find relevant tools for use case
2. GET_SCHEMAS - Get input requirements (if schemaRef returned)
3. MANAGE_CONNECTIONS - Verify/create auth
4. MULTI_EXECUTE - Execute tools
5. CREATE_RECIPE - Save for reuse (optional)

Bulk Processing Flow

1. SEARCH_TOOLS - Find data source/destination tools
2. REMOTE_WORKBENCH - Process with Python helpers:
   • run_composio_tool() - Execute Composio tools
   • invoke_llm() - AI processing
   • upload_local_file() - Export results
   • proxy_execute() - Direct API calls

Supported Apps (500+)

Communication: Slack, Discord, Teams, Gmail, Outlook, WhatsApp, Telegram Development: GitHub, GitLab, Jira, Linear, Asana, Vercel Productivity: Google Workspace, Notion, Airtable, Trello Data: Snowflake, BigQuery, Datadog, Amplitude AI: OpenAI, Anthropic, Replicate

Combined Orchestration Patterns

Pattern 1: Research + Decide + Execute

1. PAL thinkdeep - Investigate problem deeply
2. PAL consensus - Get multi-model decision
3. Rube SEARCH_TOOLS - Find execution tools
4. Rube MULTI_EXECUTE - Implement decision

Pattern 2: Review + Validate + Notify

1. PAL codereview - Review code changes
2. PAL precommit - Validate git changes
3. Rube MULTI_EXECUTE - Send notifications (Slack, email)
4. Rube CREATE_RECIPE - Save for CI/CD

Pattern 3: Debug + Fix + Verify

1. PAL debug - Root cause analysis
2. Implement fix locally
3. PAL codereview - Validate fix
4. Rube MULTI_EXECUTE - Update tickets, notify team

Pattern 4: Plan + Consensus + Automate

1. PAL planner - Create implementation plan
2. PAL consensus - Validate approach with multiple models
3. Rube MULTI_EXECUTE - Execute across apps
4. Rube MULTI_EXECUTE - Execute across apps
5. Rube CREATE_RECIPE - Save as reusable workflow

Flags

Flag Type Default Description

--pal

string

PAL tool: chat, thinkdeep, planner, consensus, codereview, precommit, debug

--rube

bool false Enable Rube MCP integration

--apps

string

Comma-separated apps for Rube

--models

string auto Models for consensus (comma-separated)

--full-validation

bool false Run all PAL validators

--save-recipe

bool false Save workflow as Rube recipe

--schedule

string

Cron expression for recipe scheduling

Behavioral Flow

• Analyze - Understand what MCP capabilities are needed

• Discover - Use RUBE_SEARCH_TOOLS for external needs, listmodels for PAL

• Plan - Create execution plan (PAL planner or RUBE_CREATE_PLAN)

• Validate - Use consensus for critical decisions

• Execute - Run PAL analysis and/or Rube tools

• Persist - Save recipes, store memory for continuity

• Report - Present findings with tool attribution

Memory & State Management

PAL Continuation

Use continuation_id to maintain context across PAL tool calls:

First call returns continuation_id

result = mcp__pal__thinkdeep(...) continuation_id = result["continuation_id"]

Subsequent calls reuse it

result = mcp__pal__thinkdeep(..., continuation_id=continuation_id)

Rube Session & Memory

Use session_id and memory for Rube continuity:

First search generates session_id

result = mcp__rube__RUBE_SEARCH_TOOLS(..., session={"generate_id": True}) session_id = result["session_id"]

Subsequent calls reuse session and build memory

result = mcp__rube__RUBE_MULTI_EXECUTE_TOOL( ..., session_id=session_id, memory={"slack": ["Channel general is C123"]} )

Examples

Multi-Model Architecture Review

/sc:mcp analyze --pal consensus --models "gpt-5.2,gemini-3-pro,deepseek"
--question "Is event sourcing appropriate for this use case?"

Automated PR Workflow

/sc:mcp automate --rube --apps github,slack
--workflow "On PR merge, post summary to #releases" --save-recipe --schedule "0 9 * * 1-5"

Full Investigation Pipeline

/sc:mcp orchestrate --pal debug --rube
--issue "Memory leak in production"
--notify slack,jira --full-validation

Guardrails

• Always search tools before executing unknown integrations

• Use consensus for decisions with >$1000 impact

• Validate schemas before multi-execute

• Store memory for frequently used IDs

• Check connection status before automation

• Use thinking_mode=high for complex PAL analysis

Error Handling

Error Recovery

PAL model unavailable Fall back to different model

Rube connection missing Prompt MANAGE_CONNECTIONS

Tool schema unknown Call GET_SCHEMAS first

Rate limited Use backoff in REMOTE_WORKBENCH

Recipe not found Search or create new

Resources

• PAL MCP: codereview, debug, consensus, thinkdeep, precommit, planner, chat, challenge, apilookup

• Rube MCP: 500+ app integrations via Composio

• Trait: mcp-pal-enabled

• Apply PAL to any agent

• Trait: mcp-rube-enabled

• Apply Rube to any agent