Pulse Workflow Server

An MCP (Model Context Protocol) server that enables Claude Code to interact with and modify Pulse workflows directly.

Overview

This MCP server exposes Pulse workflow operations as tools, allowing you to:

• Browse and select from available Pulse apps
• Discover available node types, models, tools, and datasets at runtime
• Create new workflow apps from scratch
• View and navigate workflow structure
• Add, edit, and delete nodes
• Create and remove connections between nodes
• Publish workflows
• Run individual nodes for testing
• Manage workflow features and variables

Installation

Quick Start (Recommended)

No installation required - use uvx to run directly:

# Install Claude Code skills (one-time setup)
uvx pulse-workflow-mcp install-skills

From PyPI

pip install pulse-workflow-mcp

# Or with uv
uv pip install pulse-workflow-mcp

For Development

git clone https://github.com/Pulse-Intelligence/pulse-workflow-mcp.git
cd pulse-workflow-mcp
uv pip install -e ".[dev]"

Skills

This package includes Claude Code skills for guided workflow development:

Skill	Description
/pulse	Overview and available commands
/pulse-create	Create a new workflow from description
/pulse-edit	Edit an existing workflow
/pulse-publish	Validate and publish workflow

Install skills to ~/.claude/skills/:

# Using uvx (no installation required)
uvx pulse-workflow-mcp install-skills
uvx pulse-workflow-mcp install-skills --force  # Overwrite existing
uvx pulse-workflow-mcp uninstall-skills        # Remove skills

# Or if installed via pip
pulse-workflow-mcp install-skills

Configuration

Set the following environment variables:

export PULSE_API_URL="http://localhost:5001"  # Pulse instance URL
export PULSE_API_KEY="your-api-key"           # Console API key
export PULSE_APP_ID="your-app-id"             # Optional: default app ID

Getting Your API Key

The easiest way is through the Pulse workflow editor:

1. Open any workflow in Pulse
2. Click "Connect Claude Code" button
3. The modal will generate a token for you

App ID (Optional)

PULSE_APP_ID is now optional. You can:

• Set it to always work with a specific app
• Leave it empty and use list_apps + select_app tools to choose at runtime

The app ID is in the URL when viewing a workflow:

https://your-pulse.com/app/abc123def456/workflow
                        ^^^^^^^^^^^^
                        This is your app ID

Usage with Claude Code

Recommended Configuration (uvx)

Add to ~/.claude.json - no installation required:

{
  "mcpServers": {
    "pulse-workflow": {
      "command": "uvx",
      "args": [
        "pulse-workflow-mcp"
      ],
      "env": {
        "PULSE_API_URL": "http://localhost:5001",
        "PULSE_API_KEY": "your-api-key"
      }
    }
  }
}

Alternative: Direct Command

If installed via pip:

{
  "mcpServers": {
    "pulse-workflow": {
      "command": "pulse-workflow-mcp",
      "env": {
        "PULSE_API_URL": "http://localhost:5001",
        "PULSE_API_KEY": "your-api-key"
      }
    }
  }
}

With Default App ID

If you always work with one app, add PULSE_APP_ID:

{
  "mcpServers": {
    "pulse-workflow": {
      "command": "uvx",
      "args": [
        "pulse-workflow-mcp"
      ],
      "env": {
        "PULSE_API_URL": "http://localhost:5001",
        "PULSE_API_KEY": "your-api-key",
        "PULSE_APP_ID": "your-app-id"
      }
    }
  }
}

Usage

Once configured, in Claude Code:

> List my workflow apps

Claude will use: list_apps with mode="workflow"

> Select app abc123def456

Claude will use: select_app

Available Tools

App Operations

Tool	Description
list_apps	List available Pulse apps (filter by mode, name)
select_app	Select an app to work with for subsequent operations
create_app	Create a new workflow or chat app

Discovery Operations

Tool	Description
list_node_types	List all available node types with default configs
get_node_schema	Get detailed schema for a specific node type
list_tool_providers	List available tool/plugin providers
list_tools	List tools from a provider with input/output schemas
list_models	List available AI models (LLM, embedding, etc.)
list_datasets	List available knowledge base datasets

Node Operations

Tool	Description
add_node	Add a new node to the workflow
edit_node	Modify an existing node
delete_node	Remove a node and its connections
get_node	Get details of a specific node
list_nodes	List all nodes (optionally filtered by type)

Edge Operations

Tool	Description
connect_nodes	Create a connection between two nodes
disconnect_nodes	Remove connection(s) between nodes
list_edges	List all edges in the workflow

Workflow Operations

Tool	Description
view_workflow	View the complete workflow structure
publish_workflow	Publish the draft as a new version
validate_workflow	Check workflow for errors/warnings
run_node	Execute a single node for testing

Feature Operations

Tool	Description
get_features	Get workflow feature configuration
update_features	Update workflow features
get_variables	Get environment and conversation variables

Examples

List and Select Apps

> Show me my workflow apps

Claude will use: list_apps with mode="workflow"
Returns: List of apps with IDs, names, and modes

> Select the "Customer Support" app

Claude will use: select_app with the app ID

View Current Workflow

> Show me the current workflow

Claude will use: view_workflow

Add an LLM Node

> Add an LLM node called "Summarizer" that summarizes user input.
  Connect it after the Start node.

Claude will:
1. Use list_nodes to find the Start node ID
2. Use add_node with after_node_id to create and connect the LLM node

Build a RAG Pipeline

> Create a RAG pipeline that:
  1. Retrieves from my knowledge base (dataset ID: abc123)
  2. Uses GPT-4 to answer based on the context
  3. Returns the response to the user

Claude will:
1. add_node (knowledge-retrieval)
2. add_node (llm with context)
3. connect_nodes appropriately

Publish a Version

> Publish this workflow as version 1.0

Claude will use: publish_workflow with name="v1.0"

Create a Workflow from Scratch (Discovery Flow)

> Create a new customer support workflow with an LLM that responds to questions

Claude will:
1. create_app to create a new workflow app
2. list_node_types to discover available nodes
3. get_node_schema("llm") to understand LLM node config
4. list_models to discover available AI models
5. add_node to add the LLM node with proper config
6. connect_nodes to wire up the flow
7. validate_workflow to check for errors

Configure a Knowledge Retrieval Node

> Add knowledge retrieval from my product docs

Claude will:
1. list_datasets to find available knowledge bases
2. get_node_schema("knowledge-retrieval") to get config schema
3. add_node with the discovered dataset ID

Supported Node Types

Category	Node Types
Control	start, end, answer, if-else, iteration, loop
AI	llm, knowledge-retrieval, question-classifier, parameter-extractor
Transform	code, template-transform, variable-assigner, variable-aggregator, document-extractor, list-filter, assigner
External	http-request, tool

Resources

The server provides these MCP resources:

• pulse://workflow/current - Current workflow as JSON
• pulse://workflow/node-types - Available node type schemas
• pulse://workflow/summary - Human-readable workflow summary

Prompts

Available MCP prompts:

• workflow_context - Full context about the current workflow
• discovery_workflow - Mandatory discovery workflow for building from scratch
• add_rag_pipeline - Template for adding a RAG pipeline
• add_llm_chain - Template for adding an LLM processing chain

Development

# Clone and install
git clone https://github.com/Pulse-Intelligence/pulse-workflow-mcp.git
cd pulse-workflow-mcp
uv pip install -e ".[dev]"

# Run tests
pytest tests/

# Run linter
ruff check .

# Run server locally
export PULSE_API_URL="http://localhost:5001"
export PULSE_API_KEY="your-api-key"
pulse-workflow-mcp

Architecture

┌─────────────────────────────────────────────────────────────┐
│                     Claude Code CLI                          │
│  > "Add an LLM node that summarizes user input"             │
└─────────────────────────────────────────────────────────────┘
                              │
                              │ MCP Protocol (stdio)
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                  Pulse MCP Server                           │
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────────────┐   │
│  │   Tools     │ │  Resources  │ │    Prompts          │   │
│  │ - list_apps │ │ - workflow  │ │ - workflow_context  │   │
│  │ - add_node  │ │ - node_types│ │ - add_rag_pipeline  │   │
│  │ - edit_node │ │ - summary   │ │ - add_llm_chain     │   │
│  │ - connect   │ │             │ │                     │   │
│  └─────────────┘ └─────────────┘ └─────────────────────┘   │
└─────────────────────────────────────────────────────────────┘
                              │
                              │ REST API
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                    Pulse Backend                             │
│   /apps (GET)                                               │
│   /apps/{id}/workflows/draft (GET/POST)                     │
│   /apps/{id}/workflows/publish (POST)                       │
└─────────────────────────────────────────────────────────────┘

Error Handling

The server handles common errors:

• WorkflowNotSyncError: Concurrent edit detected - refresh and retry
• PulseClientError: API errors with status code and message
• No app selected: Use list_apps and select_app to choose an app

License

MIT