About Zapiar_MCP_Server
Zapiar_MCP_Server is a lightweight MCP server for agent-driven workflow automation in Make.com and Telegram bots. It supports sequential decision flows and is described as the backend for AI-driven sales assistants.
Key features
- 01Unified MCP interface for agents
- 02Context memory for sequential decisions
- 03Response routing for AI assistants
Use cases
- 01Automating sales assistant workflows
- 02Routing agent decisions through Make.com and Telegram
MCP support
It provides an MCP interface connecting LLM agents with workflow automation systems. The README mentions Make.com integration, webhooks and REST APIs.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEZapiar MCP Server
A lightweight Model Context Protocol (MCP) server enabling agentic automation within Make.com and Telegram bots — forming the backend for AI-driven sales assistants.
🧠 Overview
Connects LLM agents to dynamic workflow automation systems, allowing real-time decision-making, contextual task execution, and adaptive responses.
⚙️ Tech Stack
- Node.js + Express
- Make.com MCP Integration
- Webhooks + REST APIs
- JSON Schema Automation
💡 Key Features
- Unified MCP interface for multi-agent communication
- Context memory for sequential decision flows
- Low-latency response routing for AI assistants
🚀 Outcome
Enabled 50% faster workflow automation in Salesman AI prototype.
📈 Learning
Understanding of AI agent communication protocols and workflow orchestration in production environments.
👤 Author
Ode Narendra Reddy
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.