Circuitry MCP Server▌
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.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
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
- Circuitry Server - Download from circuitry.dev/download
- Node.js 18+
- An MCP-compatible AI client (Claude Code, Cursor, VS Code, Gemini CLI, etc.)
Setup
1. Install & Configure Circuitry Server
- Download Circuitry Server from circuitry.dev/download
- Launch the app (appears in your system tray)
- Click the tray icon → Server → Preferences
- Click "Generate New Access Key"
- 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
| Tool | Description |
|---|---|
circuitry.status | Check connection status |
circuitry.connect | Request connection (shows permission dialog) |
Workflow Understanding
| Tool | Description |
|---|---|
workflow.getActive | Get current visible workflow info |
workflow.getStructure | Get simplified workflow structure |
workflow.resolveFlow | Resolve user reference ("this flow") to node IDs |
workflow.getNodeSummary | Get simplified node details |
Node Operations
| Tool | Description |
|---|---|
nodes.list | List all nodes in the workflow |
nodes.get | Get a node by ID |
nodes.update | Update node configuration |
nodes.delete | Delete a node |
Code Nodes
| Tool | Description |
|---|---|
code.create | Create code node (from file path with sync, OR with name+content) |
code.createBatch | Create multiple code nodes from files |
code.setCode | Update code content (syncs to source if applicable) |
Sheet Nodes
| Tool | Description |
|---|---|
sheet.create | Create a spreadsheet node with data |
sheet.setData | Replace sheet data |
Agent Delegation
| Tool | Description |
|---|---|
agent.chat | Send message to Circuitry's chat agent |
agent.createFlowchart | Ask agent to create a flowchart |
agent.poll | Poll for agent response (async) |
Configuration
Config File
Location: ~/.circuitry/mcp-config.json
{
"eserverUrl": "http://localhost:3030",
"accessKey": "your-key-here",
"configured": true
}
Environment Variables
| Variable | Description |
|---|---|
CIRCUITRY_ESERVER_URL | Override EServer URL |
CIRCUITRY_ACCESS_KEY | Override access key |
Commands
# Run setup wizard
npx @circuitry/mcp-server setup
# Check current configuration
npx @circuitry/mcp-server status
Troubleshooting
"Cannot connect to EServer"
- Check EServer is running: Look for the Circuitry icon in your system tray
- Start Circuitry Server: Download from circuitry.dev/download
- Verify URL: Run
npx @circuitry/mcp-server status
"Invalid access key"
- Create new key: Circuitry Server → Preferences → Generate New Access Key
- Re-run setup:
npx @circuitry/mcp-server setup
"No Circuitry browser client connected"
- Open Circuitry: Make sure the Circuitry app is open
- 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.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 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
4.7★★★★★66 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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