developer-tools

Circuitry MCP Server

circuitry-dev

by circuitry-dev

Connect AI coding agents to Circuitry’s visual workflow platform to convert flowcharts to code, sync project files, and

Connects AI coding agents to Circuitry's visual workflow platform, enabling them to create and sync code nodes from project files, understand user-drawn flowcharts and diagrams, generate visual flowcharts, and create data visualizations like spreadsheets and charts.

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Bidirectional sync between code and visualsInterprets hand-drawn diagrams

best for

  • / Developers who prefer visual workflow representations
  • / Teams documenting code architecture
  • / Creating technical diagrams from existing codebases

capabilities

  • / Create code nodes from project files
  • / Sync code changes to visual workflows
  • / Interpret user-drawn flowcharts and diagrams
  • / Generate visual flowcharts from code
  • / Create data visualizations and charts
  • / Build interactive spreadsheets

what it does

Connects AI agents to Circuitry's visual workflow platform to create code nodes, interpret flowcharts, and generate visual diagrams from project files.

about

Circuitry MCP Server is an official MCP server published by circuitry-dev that provides AI assistants with tools and capabilities via the Model Context Protocol. Connect AI coding agents to Circuitry’s visual workflow platform to convert flowcharts to code, sync project files, and It is categorized under developer tools.

how to install

You can install Circuitry MCP Server in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Circuitry MCP Server is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

@circuitry/mcp-server

MCP (Model Context Protocol) server that gives AI coding agents access to Circuitry - a visual workflow and diagramming platform.

What It Does

  • Visualize Code: Create code nodes from project files with bidirectional sync
  • Understand Diagrams: AI agents can comprehend user-drawn flowcharts and diagrams
  • Create Flowcharts: Generate visual flowcharts via Circuitry's chat agent
  • Data Visualization: Create spreadsheets and charts from code analysis

Prerequisites

  1. Circuitry Server - Download from circuitry.dev/download
  2. Node.js 18+
  3. An MCP-compatible AI client (Claude Code, Cursor, VS Code, Gemini CLI, etc.)

Setup

1. Install & Configure Circuitry Server

  1. Download Circuitry Server from circuitry.dev/download
  2. Launch the app (appears in your system tray)
  3. Click the tray icon → Server → Preferences
  4. Click "Generate New Access Key"
  5. Copy the key — you'll need it in the next step

2. Run MCP Setup (Required)

npx @circuitry/mcp-server setup

This will prompt you to enter:

  • EServer address — press Enter for default (http://localhost:3030)
  • Access key — paste the key you generated above

This stores your credentials in ~/.circuitry/mcp-config.json.

3. Add to Your AI Client

Claude Code

claude mcp add circuitry npx @circuitry/mcp-server

Or manually add to ~/.claude/config.json:

{
  "mcpServers": {
    "circuitry": {
      "command": "npx",
      "args": ["-y", "@circuitry/mcp-server"]
    }
  }
}

Cursor

Settings → MCP → Add New MCP Server:

{
  "mcpServers": {
    "circuitry": {
      "command": "npx",
      "args": ["-y", "@circuitry/mcp-server"]
    }
  }
}

VS Code / Copilot

code --add-mcp '{"name":"circuitry","command":"npx","args":["-y","@circuitry/mcp-server"]}'

Gemini CLI

gemini mcp add circuitry npx @circuitry/mcp-server

Cline / Windsurf

Add to your MCP configuration using the standard format above.

4. Restart Your Client

Restart your AI client to load the MCP server.

Usage Examples

Visualize Code Files

You: Show me the auth files as code nodes in Circuitry

Agent: I'll create code nodes from your auth files...
Done! Created 4 code nodes:
- auth/login.ts
- auth/logout.ts
- auth/middleware.ts
- auth/types.ts

Changes sync bidirectionally with your source files.

Understand User-Drawn Flows

You: I've drawn a flow of how I think the auth should work

Agent: I'll analyze your flow in Circuitry...

I can see you've drawn a 5-node authentication flow:
1. Start → Login Form
2. Login Form → Validate Credentials
3. Validate Credentials → branches to Success/Failure
...

Create Flowcharts

You: Create a flowchart showing the error handling flow

Agent: I'll ask Circuitry's agent to create this flowchart...

Done! Created a flowchart with 7 nodes showing:
- Error detection
- Classification (runtime vs validation)
- Logging paths
- User notification
- Recovery options

Available Tools

Connection

ToolDescription
circuitry.statusCheck connection status
circuitry.connectRequest connection (shows permission dialog)

Workflow Understanding

ToolDescription
workflow.getActiveGet current visible workflow info
workflow.getStructureGet simplified workflow structure
workflow.resolveFlowResolve user reference ("this flow") to node IDs
workflow.getNodeSummaryGet simplified node details

Node Operations

ToolDescription
nodes.listList all nodes in the workflow
nodes.getGet a node by ID
nodes.updateUpdate node configuration
nodes.deleteDelete a node

Code Nodes

ToolDescription
code.createCreate code node (from file path with sync, OR with name+content)
code.createBatchCreate multiple code nodes from files
code.setCodeUpdate code content (syncs to source if applicable)

Sheet Nodes

ToolDescription
sheet.createCreate a spreadsheet node with data
sheet.setDataReplace sheet data

Agent Delegation

ToolDescription
agent.chatSend message to Circuitry's chat agent
agent.createFlowchartAsk agent to create a flowchart
agent.pollPoll for agent response (async)

Configuration

Config File

Location: ~/.circuitry/mcp-config.json

{
  "eserverUrl": "http://localhost:3030",
  "accessKey": "your-key-here",
  "configured": true
}

Environment Variables

VariableDescription
CIRCUITRY_ESERVER_URLOverride EServer URL
CIRCUITRY_ACCESS_KEYOverride access key

Commands

# Run setup wizard
npx @circuitry/mcp-server setup

# Check current configuration
npx @circuitry/mcp-server status

Troubleshooting

"Cannot connect to EServer"

  1. Check EServer is running: Look for the Circuitry icon in your system tray
  2. Start Circuitry Server: Download from circuitry.dev/download
  3. Verify URL: Run npx @circuitry/mcp-server status

"Invalid access key"

  1. Create new key: Circuitry Server → Preferences → Generate New Access Key
  2. Re-run setup: npx @circuitry/mcp-server setup

"No Circuitry browser client connected"

  1. Open Circuitry: Make sure the Circuitry app is open
  2. Refresh: Try refreshing the Circuitry page

Development

# Clone and install
git clone https://github.com/circuitry-dev/circuitry-mcp-server.git
cd circuitry-mcp-server
npm install

# Build
npm run build

# Test locally
npx tsx src/index.ts setup
npx tsx src/index.ts status

To test local changes, point your MCP config to the built output:

{
  "mcpServers": {
    "circuitry": {
      "command": "node",
      "args": ["/path/to/circuitry-mcp-server/dist/index.js"]
    }
  }
}

License

MIT

Links

FAQ

What is the Circuitry MCP Server MCP server?
Circuitry MCP Server is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Circuitry MCP Server?
This profile displays 66 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.766 reviews
  • Benjamin Thomas· Dec 20, 2024

    I recommend Circuitry MCP Server for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Xiao Malhotra· Dec 20, 2024

    Strong directory entry: Circuitry MCP Server surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Pratham Ware· Dec 16, 2024

    Circuitry MCP Server is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Amelia Li· Dec 8, 2024

    Circuitry MCP Server reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Ishan Sharma· Dec 4, 2024

    Circuitry MCP Server is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Min Brown· Nov 27, 2024

    I recommend Circuitry MCP Server for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Noah Liu· Nov 27, 2024

    Circuitry MCP Server has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Sophia Rahman· Nov 23, 2024

    Circuitry MCP Server is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Kwame Jain· Nov 11, 2024

    Circuitry MCP Server reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Min Thomas· Nov 11, 2024

    Useful MCP listing: Circuitry MCP Server is the kind of server we cite when onboarding engineers to host + tool permissions.

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