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Flowdot MCP Server

Developer ToolsUse Caution4.2MCP RegistryLocal
Free

Server data from the Official MCP Registry

Build and run AI workflows, apps, toolkits and knowledge bases on FlowDot from any MCP client.

About

Build and run AI workflows, apps, toolkits and knowledge bases on FlowDot from any MCP client.

Security Report

4.2
Use Caution4.2High Risk

The FlowDot MCP Server is a well-structured integration layer with proper authentication requirements and reasonable permission scope for its intended purpose as a developer tool. However, several moderate security concerns exist: OAuth credential handling lacks complete validation documentation, file:// scheme support in document uploads poses path traversal risks, and the server accepts user-submitted JavaScript in custom nodes with only AST-based validation (not sandboxed execution). The codebase shows good practices (env-var token storage, no hardcoded secrets in visible code, comprehensive test coverage), but the broad permission surface—network calls to any FlowDot endpoint, file operations for document processing, and arbitrary user script evaluation—warrants caution despite being intentional for functionality. Supply chain analysis found 7 known vulnerabilities in dependencies (0 critical, 5 high severity). Package verification found 1 issue.

3 files analyzed · 16 issues found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

HTTP Network Access

Connects to external APIs or services over the internet.

env_vars

Check that this permission is expected for this type of plugin.

File System Read

Reads files on your machine. Normal for tools that analyze or process local data.

File System Write

Writes or modifies files on your machine. Check that this is expected for the tool.

process_spawn

Check that this permission is expected for this type of plugin.

What You'll Need

Set these up before or after installing:

Your FlowDot MCP token (starts with fd_mcp_). Create one free at https://flowdot.ai/mcpRequired

Environment variable: FLOWDOT_API_TOKEN

FlowDot Hub URL. Defaults to https://flowdot.aiOptional

Environment variable: FLOWDOT_HUB_URL

How to Install

Add this to your MCP configuration file:

{
  "mcpServers": {
    "ai-flowdot-mcp-server": {
      "env": {
        "FLOWDOT_HUB_URL": "your-flowdot-hub-url-here",
        "FLOWDOT_API_TOKEN": "your-flowdot-api-token-here"
      },
      "args": [
        "-y",
        "@flowdot.ai/mcp-server"
      ],
      "command": "npx"
    }
  }
}

Documentation

View on GitHub

From the project's GitHub README.

@flowdot.ai/mcp-server

Connect Claude Desktop, Cursor, Windsurf, Claude Code, and any other MCP-compatible AI client to the entire FlowDot platform — workflows, recipes, custom nodes, apps, knowledge bases, agent toolkits, and the full community/sharing layer.

What is MCP?

MCP (Model Context Protocol) is an open standard that lets AI models interact with external tools and services. The FlowDot MCP Server exposes 150 tools across 17 functional categories, plus 8 educational learn:// resources, giving an AI client a complete operational interface to FlowDot — no web UI required.

With this server, an AI client can:

  • Build workflows from scratch — create graphs, add nodes, wire connections, validate, execute, stream results
  • Author and share custom nodes — write JavaScript components, validate them with AST parsing, publish to the community
  • Develop FlowDot Apps — full React multi-file projects with surgical code editing operations
  • Design agent recipes — multi-step agentic programs with stores, gates, branches, loops, parallel steps, and sub-recipes
  • Manage knowledge bases (RAG) — categories, documents, uploads, semantic queries
  • Create and invoke agent toolkits — define new tools, configure OAuth/API-key credentials, install, invoke
  • Set up agent characters for voice calls — list/get/create/update/delete/fork/duplicate/publish, with server-side voice-config completeness validation
  • Share and discover — public URLs, voting, comments, favorites, community browsing
  • Run and observe executions — start, stream via SSE, cancel, retry, view history and metrics

Note: Recipes can be designed through MCP but must be executed via the FlowDot CLI (@flowdot.ai/cli). Recipes are long-running agentic programs that exceed AI client timeouts and require local file/code/shell access.

Quick Start

1. Get a free MCP Token

Create a free FlowDot account. You land on the MCP Tokens tab: click Select all (or pick scopes, see Token Scopes), then Create Token. The token starts with fd_mcp_, and the page shows the ready-to-paste setup below with your token filled in.

The free plan includes 5 workflow runs a day and 10 toolkit calls a day. The Creator plan is $19 a month (500 runs a month, 200 toolkit calls a day). Details: flowdot.ai/mcp.

2. Connect your client

Claude Code (one command):

claude mcp add flowdot -e FLOWDOT_API_TOKEN=fd_mcp_your_token_here -- npx -y @flowdot.ai/mcp-server

Claude Desktop, Cursor and other clients: add the block below to the client's config file.

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "flowdot": {
      "command": "npx",
      "args": ["-y", "@flowdot.ai/mcp-server"],
      "env": {
        "FLOWDOT_API_TOKEN": "fd_mcp_your_token_here"
      }
    }
  }
}

3. Restart the client

Restart Claude Desktop, Cursor or your MCP client so it loads the server. Claude Code picks it up on the next session.

Installation Options

Option A: Claude Desktop Extension (.mcpb)

Download the .mcpb extension bundle and double-click to install in Claude Desktop. The extension includes Node.js runtime and all dependencies — no separate installation needed.

Option B: npm (Cursor, Claude Code, manual config)

npx @flowdot.ai/mcp-server

Or install globally:

npm install -g @flowdot.ai/mcp-server
flowdot-mcp

Educational Resources (learn://)

The server exposes 8 standalone concept guides via the MCP ReadResourceRequest interface. Read these before invoking tools to scaffold your understanding:

ResourceContent
learn://overviewFlowDot platform overview
learn://workflowsWorkflow creation guide
learn://recipesAgent recipe orchestration guide
learn://custom-nodesCustom node development guide
learn://appsApp development guide
learn://toolkitsAgent toolkit guide
learn://knowledge-baseKnowledge base & RAG guide
learn://charactersAgent character (voice call) setup guide

Available Tools

The server exposes 150 tools organized into 17 categories.

Core (4)

  • list_workflows — List all workflows accessible to the authenticated user
  • execute_workflow — Execute a workflow with optional inputs (sync or async)
  • get_execution_status — Get the status and results of a workflow execution
  • agent_chat — Chat with the FlowDot AI agent for workflow assistance

Analytics & Feedback (3)

  • get_workflow_metrics — Impressions, success/failure rates, average duration
  • get_workflow_comments — Comments and ratings on workflows
  • get_execution_history — Past execution history with timestamps and status

Workflow Management (5)

  • get_workflow_details — Detailed workflow info including nodes, connections, signature
  • get_workflow_inputs_schema — Input schema with expected types and required fields
  • duplicate_workflow — Create a copy of an existing workflow
  • toggle_workflow_public — Make a workflow public or private
  • favorite_workflow — Add/remove workflow from favorites

Execution Enhancements (3)

  • cancel_execution — Cancel running/pending workflow executions
  • retry_execution — Retry failed executions with the same inputs
  • stream_execution — Real-time SSE streaming of workflow execution

Discovery & Search (5)

  • get_workflow_tags — Get tags associated with a workflow
  • set_workflow_tags — Set/update workflow tags
  • search_workflows — Search workflows by name, description, tags
  • search — Unified search across workflows, apps, custom nodes
  • get_public_workflows — Browse public workflows shared by other users

Workflow Building (4)

  • create_workflow — Create a new empty workflow
  • delete_workflow — Permanently delete a workflow
  • get_workflow_graph — Get complete graph structure (all nodes + connections)
  • validate_workflow — Validate for missing connections, invalid config, disconnected nodes

Node Operations (5)

  • list_available_nodes — List all node types organized by category
  • get_node_schema — Full schema for a node type (inputs, outputs, properties)
  • add_node — Add new node (built-in or custom via custom_node_{hash})
  • update_node — Update node position or properties
  • delete_node — Delete node and all its connections

Connection Operations (3)

  • add_connection — Connect a node output to a node input
  • delete_connection — Remove a connection between two nodes
  • get_node_connections — Get all connections to/from a specific node

Custom Nodes (13)

  • list_custom_nodes — List your custom nodes with search/category filtering
  • search_public_custom_nodes — Search public nodes shared by the community
  • get_custom_node — Detailed custom node info (inputs, outputs, script code)
  • get_custom_node_comments — Comments and ratings on a custom node
  • get_custom_node_template — Generate a working script template based on I/O definitions
  • create_custom_node — Create new custom node with script, inputs, outputs (validated with AST parsing)
  • update_custom_node — Update name, description, code, properties
  • delete_custom_node — Permanently delete a custom node
  • copy_custom_node — Copy a public node to your library
  • toggle_custom_node_visibility — Change visibility (private/public/unlisted)
  • vote_custom_node — Upvote/downvote/remove vote
  • favorite_custom_node — Add/remove from favorites
  • add_custom_node_comment — Add comment or reply

Apps (23)

Core App Operations (12)
  • list_apps — List your React frontend apps
  • search_apps — Search the public app marketplace
  • get_app — Detailed app info including React code and linked workflows
  • create_app — Create a new React app (Tailwind + React 18, sandboxed)
  • update_app — Update name, description, code, config, mobile settings
  • delete_app — Permanently delete app
  • publish_app — Publish to the public marketplace
  • unpublish_app — Make a published app private
  • clone_app — Clone a public app to your library
  • link_app_workflow — Link a workflow to an app for invokeWorkflow() use
  • unlink_app_workflow — Unlink a workflow from an app
  • link_app_toolkit — Link a toolkit to an app for invokeTool() use
  • unlink_app_toolkit — Unlink a toolkit from an app
  • get_app_template — Get starter code templates (basic, chat, dashboard, form-builder, data-viewer)
Surgical Code Editing (4)
  • edit_app_code — Find/replace specific strings in app code
  • append_app_code — Append content before the closing brace
  • prepend_app_code — Prepend content to the start
  • insert_app_code — Insert content after a specific pattern match
Multi-File App Operations (7)
  • list_app_files — List all files in a multi-file app
  • get_app_file — Get content of a specific file
  • create_app_file — Create new file (jsx, js, ts, tsx, css, json, md)
  • update_app_file — Update file content and type
  • delete_app_file — Delete a file
  • rename_app_file — Rename or move a file
  • set_app_entry_file — Set a file as the app's entry point

Sharing & Public URLs (9)

  • get_workflow_public_url — Public shareable URL for a workflow
  • list_shared_results — Shared execution results for a workflow
  • get_shared_result — Specific shared result with outputs/inputs
  • get_shared_result_comments — Comments on a shared result
  • create_shared_result — Create a shareable link (with optional expiry)
  • add_workflow_comment — Comment on a workflow
  • add_shared_result_comment — Comment on a shared result
  • vote_workflow — Upvote/downvote a workflow
  • vote_shared_result — Upvote/downvote a shared result

Input Presets (7)

  • list_input_presets — Pre-configured input sets for a workflow
  • get_input_preset — Specific preset with all values
  • create_input_preset — Create a shareable preset
  • update_input_preset — Update preset description/values
  • delete_input_preset — Delete a preset
  • vote_input_preset — Vote on a preset
  • toggle_community_inputs — Enable/disable community inputs for a workflow

Teams (1)

  • list_user_teams — List all teams the user belongs to (with role + member count)

Knowledge Base / RAG (14)

  • list_knowledge_categories — Document categories in your knowledge base
  • create_knowledge_category — New category with name, description, color
  • update_knowledge_category — Update category properties
  • delete_knowledge_category — Delete a category (documents become uncategorized)
  • list_knowledge_documents — Documents with category/team/status filters
  • get_knowledge_document — Document details by ID/hash
  • upload_text_document — Upload text content directly
  • upload_document_from_url — Download and add a document from a URL
  • move_document_to_category — Move a document or make it uncategorized
  • transfer_document_ownership — Transfer between personal and team knowledge base
  • reprocess_document — Reprocess a failed/stuck document
  • delete_knowledge_document — Permanently delete a document
  • query_knowledge_base — Semantic + keyword RAG search
  • get_knowledge_storage — Storage usage and limits

Agent Toolkits (24)

Agent Toolkits let an AI client create new tools through the MCP interface — effectively MCP within MCP. Once installed, toolkit tools become callable via invoke_toolkit_tool.

Toolkit Management (12)
  • list_agent_toolkits — Your toolkits
  • search_agent_toolkits — Search public toolkit marketplace
  • get_agent_toolkit — Toolkit details (tools, credentials, metadata)
  • get_toolkit_comments — Comments on a toolkit
  • create_agent_toolkit — Create a new toolkit with credential requirements
  • update_agent_toolkit — Update title, description, category, credentials
  • delete_agent_toolkit — Delete a toolkit
  • copy_agent_toolkit — Create a private copy of a public toolkit
  • toggle_toolkit_visibility — Change visibility (private/public/unlisted)
  • vote_toolkit — Vote on a toolkit
  • favorite_toolkit — Add/remove from favorites
  • add_toolkit_comment — Add comment to a toolkit
Toolkit Usage & Invocation (12)
  • install_toolkit — Install a toolkit on your account
  • uninstall_toolkit — Uninstall
  • list_installed_toolkits — Installed toolkits with credential status
  • toggle_toolkit_active — Enable/disable an installation
  • check_toolkit_credentials — Show which credentials are missing
  • update_toolkit_installation — Map toolkit credentials to your API keys
  • invoke_toolkit_tool — Execute a tool from an installed toolkit
  • list_toolkit_tools — All tools in a toolkit
  • get_toolkit_tool — Tool details with input/output schemas
  • create_toolkit_tool — Create an HTTP- or Workflow-backed tool inside a toolkit
  • update_toolkit_tool — Update tool configuration, schema, endpoint
  • delete_toolkit_tool — Delete a tool from a toolkit

Credential types supported: api_key, oauth (with PKCE + scopes + refresh tokens), bearer, basic, custom Tool types supported: http (REST API), workflow (invoke a FlowDot workflow)

Agent Recipes (19)

Recipes are reusable agentic programs with multiple step types and persistent stores. MCP can DESIGN recipes; only the CLI can RUN them.

Recipe Core (8)
  • list_recipes — List recipes (with favorites_only filter)
  • get_recipe — Recipe details with steps, stores, metadata
  • get_recipe_definition — Full recipe in YAML or JSON format
  • browse_recipes — Public recipe browsing with pagination/sorting
  • create_recipe — Create a new agent recipe
  • update_recipe — Update metadata and entry_step_id (critical for execution)
  • delete_recipe — Delete a recipe
  • fork_recipe — Create a private copy of a public recipe
Step Management (4)
  • list_recipe_steps — All steps with types, connections, config
  • add_recipe_step — Add step (agent, parallel, loop, gate, branch, invoke)
  • update_recipe_step — Update step name, description, config, connections
  • delete_recipe_step — Delete a step
Store Management (4)
  • list_recipe_stores — Stores (variables) in a recipe
  • add_recipe_store — Add a store for data flow between steps
  • update_recipe_store — Update key, label, type, default, I/O flags
  • delete_recipe_store — Delete a store
Engagement (3)
  • link_recipe — Link recipe for CLI execution with an alias
  • vote_recipe — Vote on a public recipe
  • favorite_recipe — Add/remove from favorites

Step types: agent (LLM with tools), parallel (concurrent), loop (iterate array), gate (approval checkpoint), branch (conditional), invoke (subroutine), output (emit coloured message to terminal)

Output step config: message — template string (supports {{stores.x}} interpolation), color — green | red | yellow (default green). Executes instantly with no LLM call. Use it to emit progress updates, warnings, or final summaries during long-running recipes.

To execute a recipe, use the FlowDot CLI:

npx @flowdot.ai/cli recipes run <aliasOrHash> --input '{"key":"value"}'

Agent Characters (8)

Voice-call personas — name + persona prompt + complete provider stack (TTS / STT / LLM). The Hub server-side validates voice-config completeness against the same App\Support\AgentCharacterCompleteness helper the runtime uses, so every read of a character carries an is_complete flag plus a missing_fields[] list. Read learn://characters for the per-provider settings shapes.

  • list_agent_characters — List your characters with completeness badges
  • get_agent_character — Full detail with per-field Completeness section
  • create_agent_character — Create a new character (rejects with CHARACTER_VOICE_CONFIG_INCOMPLETE 422 if any required field is missing)
  • update_agent_character — Partial update with post-merge completeness validation
  • delete_agent_character — Hard delete (requires confirm: true)
  • fork_agent_character — Copy a public character; LLM choice resets to default
  • duplicate_agent_character — Copy your own character including LLM choice
  • toggle_agent_character_public — Flip public/private (auto-mints stable hash on first publish)

Required fields: voice_provider, voice_id, tts_model, voice_settings, stt_provider, stt_model, llm_provider, llm_model, llm_temperature, personality_prompt. See learn://characters for recommended values per provider.

Token Scopes

When creating an MCP token in FlowDot Settings, you can select exactly which scopes to grant. Restrict tokens to the minimum scope they need:

Scope namespaceTools it covers
workflows:readList, search, get, view public workflows
workflows:executeExecute workflows
workflows:manageCreate, update, delete, validate, build workflow graphs
executions:readStatus, history, stream
executions:manageCancel, retry
agent:chatAgent chat
custom_nodes:readList, search, get, get template
custom_nodes:manageCreate, update, delete, copy, toggle visibility, vote, comment
apps:readList, search, get apps and files
apps:manageCreate, update, delete, publish, code editing, file management
recipes:readList, get, browse, get definition
recipes:manageCreate, update, delete, fork, link, manage steps and stores
agent_characters:readList and view agent characters (with completeness state)
agent_characters:manageCreate, edit, delete, fork, duplicate, and publish agent characters
knowledge:readList, get, query
knowledge:manageUpload, delete, categorize, transfer, reprocess
agent_toolkits:readList, search, get toolkits and tools
agent_toolkits:manageCreate, update, delete, install, invoke
sharing:readGet shared results, public URLs
sharing:manageCreate shared results, vote, comment
input_presets:readList, get presets
input_presets:manageCreate, update, delete
teams:readList teams
discovery:readSearch, tags, public browsing
analytics:readMetrics, comments, history

Environment Variables

VariableRequiredDefaultDescription
FLOWDOT_API_TOKENYes—Your MCP token (must start with fd_mcp_)
FLOWDOT_HUB_URLNohttps://flowdot.aiFlowDot Hub URL (override for self-hosted)
INTERNAL_API_SECRETNo—Optional shared secret for internal API calls

Configuring Other MCP Clients

Cursor

Settings > MCP Servers > Add Server:

{
  "flowdot": {
    "command": "npx",
    "args": ["@flowdot.ai/mcp-server"],
    "env": {
      "FLOWDOT_API_TOKEN": "fd_mcp_your_token_here"
    }
  }
}

Windsurf

Same configuration as Cursor — see the Windsurf documentation for the exact location of the MCP config file.

Claude Code

Add to your Claude Code MCP configuration (typically ~/.config/claude-code/mcp.json):

{
  "mcpServers": {
    "flowdot": {
      "command": "npx",
      "args": ["@flowdot.ai/mcp-server"],
      "env": { "FLOWDOT_API_TOKEN": "fd_mcp_your_token_here" }
    }
  }
}

Development

# Install dependencies
npm install

# Build
npm run build

# Run locally
FLOWDOT_API_TOKEN=fd_mcp_xxx npm start

# Watch mode
npm run dev

# Run tests
npm test

# Coverage report
npm run test:coverage

# Build the .mcpb Claude Desktop extension bundle
npm run build:mcpb

Architecture

The MCP server is a thin protocol adapter. All HTTP communication with the FlowDot Hub is delegated to a shared @flowdot.ai/api package, which is also used by the FlowDot CLI and daemon. This means the same client logic, authentication, retry semantics, and pagination apply across every FlowDot surface.

Source Layout

mcp-server/
├── bin/
│   └── flowdot-mcp.js          # Executable wrapper
├── src/
│   ├── index.ts                # Entry point
│   ├── server.ts               # createServer() / startServer()
│   ├── api-client.ts           # FlowDotApiClient re-export
│   ├── tools/
│   │   ├── index.ts            # Central registry (one switch case per tool)
│   │   └── *.ts                # 119 individual tool source files
│   └── utils/
│       └── script-validator.ts # AST-based custom node script validation
├── manifest.json               # MCPB Claude Desktop bundle manifest
├── scripts/
│   └── build-mcpb.js           # Builds the .mcpb extension archive
└── package.json

Custom Node Script Validation

User-submitted custom node JavaScript is validated using acorn AST parsing, not regex. The validator checks:

  • Syntax correctness
  • Required processData(inputs, properties, llm) function exists
  • Return statement exists and matches declared output keys exactly
  • No top-level return statements
  • Security patterns (no eval, process, global, require, etc.)
  • Best practices (unused inputs, unhandled errors)

The script validator has its own test suite with 100% coverage thresholds enforced via vitest.config.ts.

License

See LICENSE.

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