Make▌
by integromat
Connect AI assistants to Make automation — trigger Make scenarios via API, pass parameters and get structured JSON from
Connects AI systems to Make automation workflows, enabling assistants to trigger scenarios with parameters and receive structured JSON output from your existing Make account.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Automation engineers exposing Make workflows to AI
- / Building AI assistants that need workflow integration
- / Creating bidirectional AI-automation communication
capabilities
- / Trigger Make scenarios from AI assistants
- / Pass parameters to automation workflows
- / Receive structured JSON output from scenarios
- / Access all on-demand scheduled scenarios
- / Parse scenario input parameters automatically
what it does
Connects AI assistants to your Make automation workflows, allowing them to trigger on-demand scenarios with parameters and receive structured results.
about
Make is an official MCP server published by integromat that provides AI assistants with tools and capabilities via the Model Context Protocol. Connect AI assistants to Make automation — trigger Make scenarios via API, pass parameters and get structured JSON from It is categorized under developer tools.
how to install
You can install Make 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 supports remote connections over HTTP, so no local installation is required.
license
MIT
Make is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
Make MCP Server (legacy)
A modern, cloud-based version of the Make MCP Server is now available. For most use cases, we recommend using this new version.
A Model Context Protocol server that enables Make scenarios to be utilized as tools by AI assistants. This integration allows AI systems to trigger and interact with your Make automation workflows.
How It Works
The MCP server:
- Connects to your Make account and identifies all scenarios configured with "On-Demand" scheduling
- Parses and resolves input parameters for each scenario, providing AI assistants with meaningful parameter descriptions
- Allows AI assistants to invoke scenarios with appropriate parameters
- Returns scenario output as structured JSON, enabling AI assistants to properly interpret the results
Benefits
- Turn your Make scenarios into callable tools for AI assistants
- Maintain complex automation logic in Make while exposing functionality to AI systems
- Create bidirectional communication between your AI assistants and your existing automation workflows
Usage with Claude Desktop
Prerequisites
- NodeJS
- MCP Client (like Claude Desktop App)
- Make API Key with
scenarios:readandscenarios:runscopes
Installation
To use this server with the Claude Desktop app, add the following configuration to the "mcpServers" section of your claude_desktop_config.json:
{
"mcpServers": {
"make": {
"command": "npx",
"args": ["-y", "@makehq/mcp-server"],
"env": {
"MAKE_API_KEY": "<your-api-key>",
"MAKE_ZONE": "<your-zone>",
"MAKE_TEAM": "<your-team-id>"
}
}
}
}
MAKE_API_KEY- You can generate an API key in your Make profile.MAKE_ZONE- The zone your organization is hosted in (e.g.,eu2.make.com).MAKE_TEAM- You can find the ID in the URL of the Team page.
FAQ
- What is the Make MCP server?
- Make 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 Make?
- This profile displays 71 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 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
4.6★★★★★71 reviews- ★★★★★Ganesh Mohane· Dec 24, 2024
Strong directory entry: Make surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Olivia Okafor· Dec 24, 2024
Strong directory entry: Make surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Hana Farah· Dec 24, 2024
We wired Make into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Advait Martin· Dec 20, 2024
We evaluated Make against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Ren Singh· Dec 16, 2024
Make is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Ren Patel· Dec 12, 2024
Make is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Olivia Mensah· Dec 8, 2024
According to our notes, Make benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Advait Harris· Nov 27, 2024
I recommend Make for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Rahul Santra· Nov 15, 2024
Make has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Sofia Smith· Nov 15, 2024
Make has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
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