What is Zntl Mcp Server

The zntl-mcp-server is an implementation of a Model Context Protocol (MCP) server designed for the Transcripter project, providing tools and resources for AI-powered transcription features.

Use cases

Use cases include automated transcription services for meetings, generating summaries of recorded conversations, and searching through large volumes of transcriptions for specific information.

How to use

To use zntl-mcp-server, install it via npm, build the project, and run the server. You can interact with the server using the provided tools and resources through API calls.

Key features

Key features include a test API for endpoint testing, transcription search with filtering and pagination, and AI-generated transcription summaries. It also provides access to transcription and analysis data by ID.

Where to use

The zntl-mcp-server is suitable for applications in the fields of AI, data analysis, and transcription services, particularly where automated transcription and analysis are needed.

MCP Server for Transcripter

A Model Context Protocol (MCP) server implementation for the Transcripter project. This package provides tools and resources for AI-powered features using the MCP standard.

Features

Tools

• test-api: Test API endpoints and return the results
• transcription-search: Search transcriptions with filtering and pagination
• transcription-summary: Generate a summary of a transcription using AI

Resources

• transcription://{id}: Access transcription data by ID
• analysis://{id}: Access analysis data by ID

Requirements

• Node.js >= 18.0.0
• npm >= 7.0.0

Installation

npm install

Building

# Build for both ESM and CommonJS
npm run build

# Build for ESM only
npm run build:esm

# Build for CommonJS only
npm run build:cjs

Running

# Start the MCP server on the default port (3500)
npm run server

# Start the MCP server on a custom port
npm run server 4000

Testing

npm test

Usage Examples

Using the test-api tool

import { Client } from "@modelcontextprotocol/sdk/client";
import { SSEClientTransport } from "@modelcontextprotocol/sdk/client/sse";

async function testApiEndpoint() {
  // Connect to the MCP server
  const transport = new SSEClientTransport("http://localhost:3500/sse", "http://localhost:3500/message");
  const client = new Client();
  await client.connect(transport);
  
  // Use the test-api tool
  const result = await client.tools.execute("test-api", {
    endpoint: "transcriptions",
    method: "GET",
  });
  
  console.log(result);
}

Using the transcription resource

import { Client } from "@modelcontextprotocol/sdk/client";
import { SSEClientTransport } from "@modelcontextprotocol/sdk/client/sse";

async function getTranscription(id: number) {
  // Connect to the MCP server
  const transport = new SSEClientTransport("http://localhost:3500/sse", "http://localhost:3500/message");
  const client = new Client();
  await client.connect(transport);
  
  // Access the transcription resource
  const transcription = await client.resources.get(`transcription://${id}`);
  
  console.log(transcription);
}

Integration with Transcripter

This MCP server integrates with the Transcripter project to provide AI-powered features for transcriptions and analyses. It serves as a standardized interface for AI model interactions.

Project Structure

• src/cli.ts: Command-line interface for starting the MCP server
• src/tools/: Implementation of MCP tools
• src/resources/: Implementation of MCP resource providers
• src/tests/: Tests for tools and resources

License

MIT