Workflow MCP - Intelligent Task Orchestration

Overview

The Workflow MCP uses Claude as an intelligent orchestrator to execute multi-step workflows, make decisions, and coordinate other SimpleMCPs. It's like having an AI assistant that can automate complex tasks by combining multiple tools.

What Makes This Powerful

Claude-Powered Intelligence: Each step can use Claude's reasoning to analyze results and make decisions
Tool Orchestration: Calls other SimpleMCPs as tools (GitHub, Slack, databases, cloud storage, etc.)
Conditional Logic: Steps can execute based on previous results
State Management: Tracks workflow execution and results
Natural Language Workflows: Generate workflows from plain English descriptions

Installation & Configuration

1. Configure Anthropic API Key

json

{
  "mcpServers": {
    "workflow": {
      "type": "internal",
      "env": {
        "ANTHROPIC_API_KEY": "_USE_SECURE_STORAGE_"
      }
    }
  }
}

Get your API key from: https://console.anthropic.com/

2. Verify Installation

bash

ncp find "workflow"

Core Capabilities

1. Execute Predefined Workflows

Run built-in workflows for common tasks:

bash

# Daily standup: GitHub issues → AI summary → Slack
ncp run workflow:execute \
  workflowName="daily-standup" \
  context='{"slackWebhook":"https://hooks.slack.com/...","repo":"owner/repo"}'

# Content pipeline: Scrape → Analyze → Report → Email
ncp run workflow:execute \
  workflowName="content-pipeline" \
  context='{"sourceUrl":"https://...","recipient":"user@example.com"}'

# Data sync: Database backup → S3 upload → Notify
ncp run workflow:execute \
  workflowName="data-sync" \
  context='{"dbPath":"./data.db","s3Bucket":"backups","slackWebhook":"..."}'

2. Execute Custom Workflows

Define and run your own workflows:

bash

ncp run workflow:execute-custom \
  workflow='{
    "name": "content-analyzer",
    "description": "Analyze website content and save insights",
    "steps": [
      {
        "name": "Scrape Website",
        "description": "Extract content from target URL",
        "tool": "scraper:extract",
        "parameters": {"url": "https://example.com"}
      },
      {
        "name": "Analyze with AI",
        "description": "Extract key insights",
        "tool": "ai:complete",
        "parameters": {
          "prompt": "Analyze this content and extract key insights",
          "provider": "anthropic"
        }
      },
      {
        "name": "Save to Database",
        "description": "Store insights in database",
        "tool": "database:insert",
        "parameters": {
          "dbPath": "./insights.db",
          "tableName": "content_insights"
        }
      }
    ]
  }'

3. Execute Intelligent Tasks

Let Claude figure out how to accomplish a task:

bash

# Simple task
ncp run workflow:execute-task \
  task="Summarize the latest trends in AI from Hacker News"

# Task with context
ncp run workflow:execute-task \
  task="Find and summarize GitHub issues about performance" \
  context='{"repo":"facebook/react","daysBack":7}'

# Task with tool restrictions
ncp run workflow:execute-task \
  task="Create a daily report and send it via email" \
  availableTools='["ai:complete","notify:email","database:query"]'

4. Generate Workflows from Natural Language

Describe what you want, get a workflow definition:

bash

ncp run workflow:generate-workflow \
  description="Every morning, check GitHub issues in my repo, summarize them with AI, and post a summary to our team's Slack channel"

# Returns:
{
  "workflow": {
    "name": "morning-github-summary",
    "description": "Daily GitHub issue summary to Slack",
    "steps": [...]
  }
}

# Then execute the generated workflow:
ncp run workflow:execute-custom workflow='<paste-generated-workflow>'

5. Schedule Workflows

Get cron commands for scheduling:

bash

ncp run workflow:schedule \
  workflowName="daily-standup" \
  schedule="0 9 * * 1-5"

# Returns instructions like:
# Add to crontab: 0 9 * * 1-5 ncp run workflow:execute workflowName="daily-standup"

Built-in Workflows

1. Daily Standup (`daily-standup`)

What it does: Fetches GitHub issues, summarizes with AI, posts to Slack

Required context:

slackWebhook - Slack webhook URL
repo - GitHub repository (owner/repo)

Example:

bash

ncp run workflow:execute \
  workflowName="daily-standup" \
  context='{
    "slackWebhook": "https://hooks.slack.com/services/...",
    "repo": "anthropics/claude-code"
  }'

Steps:

Fetch open GitHub issues
AI summarizes issues for standup
Post summary to Slack

2. Content Pipeline (`content-pipeline`)

What it does: Scrapes content, analyzes with AI, generates PDF report, emails results

Required context:

sourceUrl - URL to scrape
recipient - Email recipient

Example:

bash

ncp run workflow:execute \
  workflowName="content-pipeline" \
  context='{
    "sourceUrl": "https://news.ycombinator.com",
    "recipient": "team@company.com"
  }'

Steps:

Scrape content from URL
AI analyzes content for insights
Generate PDF report
Email report to recipient

3. Data Sync (`data-sync`)

What it does: Backs up database to S3, notifies on completion

Required context:

dbPath - SQLite database path
s3Bucket - S3 bucket name
slackWebhook - Slack webhook for notifications

Example:

bash

ncp run workflow:execute \
  workflowName="data-sync" \
  context='{
    "dbPath": "./production.db",
    "s3Bucket": "my-backups",
    "slackWebhook": "https://hooks.slack.com/..."
  }'

Steps:

Export database to JSON
Upload to S3
Notify Slack on success

Creating Custom Workflows

Workflow Definition Structure

typescript

{
  "name": "workflow-name",
  "description": "What this workflow does",
  "steps": [
    {
      "name": "Step Name",
      "description": "What this step does",
      "tool": "mcp-name:tool-name",      // Optional: MCP tool to call
      "parameters": {                     // Optional: Tool parameters
        "param1": "value1",
        "param2": "${context.variable}"   // Use context variables
      },
      "condition": "expression"           // Optional: Execute conditionally
    }
  ]
}

Using Context Variables

Access context and previous results in parameters:

json

{
  "parameters": {
    "url": "${context.websiteUrl}",              // From context
    "content": "${previousResults[0].output}",   // From previous step
    "timestamp": "${timestamp}"                   // Built-in variable
  }
}

Conditional Steps

Execute steps based on conditions:

json

{
  "name": "Send Alert",
  "description": "Alert if error count > 10",
  "tool": "notify:slack",
  "parameters": {...},
  "condition": "previousResults[0].output.errorCount > 10"
}

Available SimpleMCP Tools

AI & Analysis

ai:complete - Generate text with AI
ai:vision - Analyze images
ai:similarity - Compare text similarity
ai:embed - Generate embeddings

Notifications

notify:slack - Send Slack messages
notify:discord - Send Discord messages
notify:email - Send emails
notify:teams - Send Teams messages
notify:broadcast - Send to multiple platforms

Data & Storage

database:query - Execute SQL queries
database:insert - Insert data
database:find - Query with filters
database:backup - Backup database
cloud:s3-upload - Upload to S3
cloud:s3-download - Download from S3
cloud:gcs-upload - Upload to Google Cloud
cloud:azure-upload - Upload to Azure

Web & Scraping

scraper:extract - Extract content from URLs
scraper:screenshot - Capture screenshots
scraper:fill-form - Automate forms
scraper:extract-links - Get all links

Documents

document:create-pdf - Generate PDFs
document:create-excel - Generate spreadsheets
document:create-word - Generate Word docs
document:parse-csv - Parse CSV files
document:create-invoice - Generate invoices

Version Control

github:create-issue - Create GitHub issues
github:list-issues - List repository issues

Real-World Examples

Example 1: Automated Content Monitoring

Monitor a website and get notified of changes:

json

{
  "name": "content-monitor",
  "description": "Monitor website, detect changes, notify",
  "steps": [
    {
      "name": "Scrape Current Content",
      "tool": "scraper:extract",
      "parameters": {"url": "https://example.com/news"}
    },
    {
      "name": "Compare with Previous",
      "tool": "ai:similarity",
      "parameters": {
        "text1": "${context.previousContent}",
        "text2": "${previousResults[0].output}"
      }
    },
    {
      "name": "Alert on Changes",
      "tool": "notify:slack",
      "parameters": {
        "webhookUrl": "${context.slackWebhook}",
        "text": "Content changed! Similarity: ${previousResults[1].similarity}"
      },
      "condition": "previousResults[1].similarity < 0.9"
    }
  ]
}

Example 2: Data Pipeline

ETL pipeline with AI enrichment:

json

{
  "name": "data-pipeline",
  "description": "Extract, enrich with AI, load to database",
  "steps": [
    {
      "name": "Extract from API",
      "tool": "scraper:extract",
      "parameters": {"url": "https://api.example.com/data"}
    },
    {
      "name": "Enrich with AI",
      "tool": "ai:complete",
      "parameters": {
        "prompt": "Categorize and tag this data: ${previousResults[0].output}",
        "provider": "anthropic"
      }
    },
    {
      "name": "Load to Database",
      "tool": "database:insert",
      "parameters": {
        "dbPath": "./analytics.db",
        "tableName": "enriched_data",
        "data": "${previousResults[1].output}"
      }
    },
    {
      "name": "Generate Report",
      "tool": "document:create-pdf",
      "parameters": {
        "title": "Daily Analytics Report",
        "content": "${previousResults[1].output}"
      }
    }
  ]
}

Example 3: Weekly Team Report

Aggregate data from multiple sources:

json

{
  "name": "weekly-report",
  "description": "Compile weekly team report from GitHub, database, and analytics",
  "steps": [
    {
      "name": "Get GitHub Activity",
      "tool": "github:list-issues",
      "parameters": {"repo": "team/project", "state": "closed"}
    },
    {
      "name": "Query Database Metrics",
      "tool": "database:query",
      "parameters": {
        "dbPath": "./metrics.db",
        "query": "SELECT * FROM weekly_stats WHERE week = '${context.week}'"
      }
    },
    {
      "name": "Synthesize with AI",
      "tool": "ai:complete",
      "parameters": {
        "prompt": "Create executive summary from: GitHub: ${previousResults[0]}, Metrics: ${previousResults[1]}",
        "provider": "anthropic"
      }
    },
    {
      "name": "Create PDF Report",
      "tool": "document:create-report",
      "parameters": {
        "title": "Weekly Team Report",
        "summary": "${previousResults[2].output}",
        "data": "${previousResults[1].output}"
      }
    },
    {
      "name": "Email to Team",
      "tool": "notify:email",
      "parameters": {
        "to": "${context.teamEmail}",
        "subject": "Weekly Report - Week ${context.week}",
        "text": "${previousResults[2].output}"
      }
    }
  ]
}

Scheduling Workflows with NCP Schedule MCP

The Workflow MCP is designed to be invoked by the Schedule MCP for automated execution.

Setup: Schedule MCP + Workflow MCP

Architecture:

Schedule MCP - Handles WHEN to run (timing, cron)
Workflow MCP - Handles WHAT to run (intelligent task execution)

Step 1: List Available Workflows

bash

ncp run workflow:list-workflows

Returns:

json

{
  "workflows": [
    {
      "name": "daily-standup",
      "description": "Daily standup: Check issues, summarize, post to Slack",
      "requiredContext": ["slackWebhook", "repo"],
      "recommendedSchedule": "0 9 * * 1-5"
    }
  ]
}

Step 2: Add to Schedule MCP

bash

# Schedule daily standup for weekdays at 9am
ncp run schedule:create \
  name="daily-standup" \
  tool="workflow:execute" \
  parameters='{"workflowName":"daily-standup", "context": {"slackWebhook":"https://hooks.slack.com/...", "repo":"owner/repo"}}' \
  schedule="0 9 * * 1-5"

# Schedule content pipeline daily at midnight
ncp run schedule:create \
  name="content-pipeline" \
  tool="workflow:execute" \
  parameters='{"workflowName":"content-pipeline", "context": {"sourceUrl":"https://...", "recipient":"user@example.com"}}' \
  schedule="0 0 * * *"

# Schedule data sync daily at 2am
ncp run schedule:create \
  name="data-sync" \
  tool="workflow:execute" \
  parameters='{"workflowName":"data-sync", "context": {"dbPath":"./data.db", "s3Bucket":"backups", "slackWebhook":"..."}}' \
  schedule="0 2 * * *"

Step 3: Verify Schedule

bash

# List all scheduled tasks
ncp run schedule:list

# Check specific schedule
ncp run schedule:get name="daily-standup"

Step 4: Run Manually (Test Before Scheduling)

Always test your workflow manually first:

bash

# Test the workflow command
ncp run workflow:execute \
  workflowName="daily-standup" \
  context='{"slackWebhook":"https://...","repo":"owner/repo"}'

# If it works, add to schedule

Example: Complete Setup Flow

bash

# 1. Test workflow manually
ncp run workflow:execute \
  workflowName="daily-standup" \
  context='{"slackWebhook":"https://hooks.slack.com/services/T00/B00/XXX","repo":"anthropics/claude-code"}'

# 2. If successful, schedule it
ncp run schedule:create \
  name="team-standup" \
  tool="workflow:execute" \
  parameters='{"workflowName":"daily-standup", "context": {"slackWebhook":"https://hooks.slack.com/services/T00/B00/XXX", "repo":"anthropics/claude-code"}}' \
  schedule="0 9 * * 1-5" \
  description="Daily standup summary posted to Slack"

# 3. Verify it's scheduled
ncp run schedule:list

# 4. Remove if needed
ncp run schedule:delete job_id="team-standup"

Advanced: Multiple Workflows on Different Schedules

bash

# Morning standup
ncp run schedule:create \
  name="morning-standup" \
  tool="workflow:execute" \
  parameters='{"workflowName":"daily-standup", "context": "..."}' \
  schedule="0 9 * * 1-5"

# Hourly content check
ncp run schedule:create \
  name="hourly-content-monitor" \
  tool="workflow:execute-task" \
  parameters='{"task": "Check website for changes and alert if needed", "context": "..."}' \
  schedule="0 * * * *"

# Nightly backup
ncp run schedule:create \
  name="nightly-backup" \
  tool="workflow:execute" \
  parameters='{"workflowName":"data-sync", "context": "..."}' \
  schedule="0 2 * * *"

# Weekly report
ncp run schedule:create \
  name="weekly-report" \
  tool="workflow:execute-custom" \
  parameters='{"workflow": "..."}' \
  schedule="0 9 * * 1"

Advanced Features

Error Handling

Workflows automatically stop on errors and report:

json

{
  "workflowName": "daily-standup",
  "status": "failed",
  "results": [
    {"step": "Fetch Issues", "success": true, "output": {...}},
    {"step": "Summarize", "success": false, "error": "API key invalid"}
  ]
}

Workflow State

Every execution returns complete state:

json

{
  "workflowName": "content-pipeline",
  "currentStep": 3,
  "status": "completed",
  "startTime": "2024-01-15T09:00:00Z",
  "endTime": "2024-01-15T09:02:30Z",
  "results": [
    {
      "step": "Scrape Content",
      "success": true,
      "output": {...},
      "timestamp": "2024-01-15T09:00:15Z"
    },
    // ... more results
  ]
}

Conditional Execution

Steps execute only when conditions are met:

json

{
  "name": "Alert on High Error Rate",
  "tool": "notify:slack",
  "parameters": {...},
  "condition": "previousResults[0].output.errorRate > 0.05"
}

Best Practices

1. Start Simple

Begin with 2-3 steps, add complexity gradually:

json

{
  "steps": [
    {"name": "Fetch data", "tool": "..."},
    {"name": "Process", "tool": "ai:complete"},
    {"name": "Notify", "tool": "notify:slack"}
  ]
}

2. Use Context for Configuration

Pass environment-specific values via context:

bash

# Development
ncp run workflow:execute workflowName="..." context='{"env":"dev","dbPath":"./dev.db"}'

# Production
ncp run workflow:execute workflowName="..." context='{"env":"prod","dbPath":"./prod.db"}'

3. Test Steps Individually

Before creating a workflow, test each tool separately:

bash

# Test scraping
ncp run scraper:extract url="https://example.com"

# Test AI
ncp run ai:complete prompt="Summarize this..." provider="anthropic"

# Then combine in workflow

4. Use Descriptive Names

Make workflows self-documenting:

json

{
  "name": "github-issues-to-slack-summary",
  "description": "Fetch GitHub issues, generate AI summary, post to Slack",
  "steps": [
    {"name": "Fetch Open Issues from Repository", ...},
    {"name": "Generate Natural Language Summary", ...},
    {"name": "Post Summary to Team Channel", ...}
  ]
}

5. Handle Failures Gracefully

Add notification steps with conditions:

json

{
  "name": "Notify on Failure",
  "tool": "notify:slack",
  "parameters": {
    "text": "Workflow failed at step: ${context.failedStep}"
  },
  "condition": "status === 'failed'"
}

Troubleshooting

Workflow Not Executing

Check API key is configured:

bash

# Should show ANTHROPIC_API_KEY
cat ~/.ncp/all.json | grep ANTHROPIC

Verify workflow exists:

bash

ncp run workflow:execute workflowName="invalid-name"
# Shows available workflows

Tool Calls Failing

Ensure tool MCPs are loaded:

bash

ncp list  # Should show github, notify, etc.

Check tool parameters match schema:

bash

ncp find "github:list-issues"  # Shows required parameters

Context Variables Not Working

Use correct syntax:

${context.variableName} - From context parameter
${previousResults[0].output} - From previous step
${timestamp} - Built-in variables

Summary

The Workflow MCP turns Claude into an intelligent task orchestrator that can:

✅ Execute multi-step workflows with intelligent decision-making ✅ Coordinate multiple SimpleMCPs as tools ✅ Handle conditional logic and error cases ✅ Generate workflows from natural language ✅ Be scheduled for automated recurring tasks

Key Innovation: Claude isn't just executing steps—it's reasoning about the results and making intelligent decisions at each stage.

Start with built-in workflows, then create your own custom automations!

Workflow MCP - Intelligent Task Orchestration ​

Overview ​

What Makes This Powerful ​

Installation & Configuration ​

1. Configure Anthropic API Key ​

2. Verify Installation ​

Core Capabilities ​

1. Execute Predefined Workflows ​

2. Execute Custom Workflows ​

3. Execute Intelligent Tasks ​

4. Generate Workflows from Natural Language ​

5. Schedule Workflows ​

Built-in Workflows ​

1. Daily Standup (daily-standup) ​

2. Content Pipeline (content-pipeline) ​

3. Data Sync (data-sync) ​

Creating Custom Workflows ​

Workflow Definition Structure ​

Using Context Variables ​

Conditional Steps ​

Available SimpleMCP Tools ​

AI & Analysis ​

Notifications ​

Data & Storage ​

Web & Scraping ​

Documents ​

Version Control ​

Real-World Examples ​

Example 1: Automated Content Monitoring ​

Example 2: Data Pipeline ​

Example 3: Weekly Team Report ​

Scheduling Workflows with NCP Schedule MCP ​

Setup: Schedule MCP + Workflow MCP ​

Step 1: List Available Workflows ​

Step 2: Add to Schedule MCP ​

Step 3: Verify Schedule ​

Step 4: Run Manually (Test Before Scheduling) ​

Example: Complete Setup Flow ​

Advanced: Multiple Workflows on Different Schedules ​

Advanced Features ​

Error Handling ​

Workflow State ​

Conditional Execution ​

Best Practices ​

1. Start Simple ​

2. Use Context for Configuration ​

3. Test Steps Individually ​

4. Use Descriptive Names ​

5. Handle Failures Gracefully ​

Troubleshooting ​

Workflow Not Executing ​

Tool Calls Failing ​

Context Variables Not Working ​

Summary ​

Workflow MCP - Intelligent Task Orchestration

Overview

What Makes This Powerful

Installation & Configuration

1. Configure Anthropic API Key

2. Verify Installation

Core Capabilities

1. Execute Predefined Workflows

2. Execute Custom Workflows

3. Execute Intelligent Tasks

4. Generate Workflows from Natural Language

5. Schedule Workflows

Built-in Workflows

1. Daily Standup (`daily-standup`)

2. Content Pipeline (`content-pipeline`)

3. Data Sync (`data-sync`)

Creating Custom Workflows

Workflow Definition Structure

Using Context Variables

Conditional Steps

Available SimpleMCP Tools

AI & Analysis

Notifications

Data & Storage

Web & Scraping

Documents

Version Control

Real-World Examples

Example 1: Automated Content Monitoring

Example 2: Data Pipeline

Example 3: Weekly Team Report

Scheduling Workflows with NCP Schedule MCP

Setup: Schedule MCP + Workflow MCP

Step 1: List Available Workflows

Step 2: Add to Schedule MCP

Step 3: Verify Schedule

Step 4: Run Manually (Test Before Scheduling)

Example: Complete Setup Flow

Advanced: Multiple Workflows on Different Schedules

Advanced Features

Error Handling

Workflow State

Conditional Execution

Best Practices

1. Start Simple

2. Use Context for Configuration

3. Test Steps Individually

4. Use Descriptive Names

5. Handle Failures Gracefully

Troubleshooting

Workflow Not Executing

Tool Calls Failing

Context Variables Not Working

Summary