Example MCP server demonstrating framework usage
Connect Example to Claude, Cursor or any other MCP client and it stops being a tab you switch to. Example MCP server demonstrating framework usage. The example mcp server is what makes that connection.
A production-ready MCP (Model Context Protocol) server built with TypeScript featuring both basic tools and advanced LLM-powered capabilities with type-safe provider and model selection and dual-mode operation (STDIO + Streamable HTTP with OAuth).
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
hello — Greets a person by nameecho — Echoes back a provided messagecurrent-time — Returns the current timestampchat — Interactive AI assistant using Claude Haiku (fast responses)analyze — Deep text analysis using GPT-4 (sentiment, themes, structure)summarize — Text summarization using Gemini Flash (cost-effective)explain — Educational explanations using Claude (clear, adaptive to level)OpenAI — https://platform.openai.com/api-keysFeatures — The Features tool exposed by this serverDocumentation — The Documentation tool exposed by this serverInstallation goes through your MCP client rather than a global install: point it at tsx on npm and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Configuration is passed through the environment: ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY, GOOGLE_CLIENT_ID, GITHUB_CLIENT_ID, MICROSOFT_CLIENT_ID. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Example's toolset — hello, echo, current-time and 7 more — is a fair guide to whether it matches your workflow. It is maintained by jeffrdutton; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Example's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| hello | Greets a person by name |
| echo | Echoes back a provided message |
| current-time | Returns the current timestamp |
| chat | Interactive AI assistant using Claude Haiku (fast responses) |
| analyze | Deep text analysis using GPT-4 (sentiment, themes, structure) |
| summarize | Text summarization using Gemini Flash (cost-effective) |
| explain | Educational explanations using Claude (clear, adaptive to level) |
| OpenAI | https://platform.openai.com/api-keys |
| Features | The Features tool exposed by this server. |
| Documentation | The Documentation tool exposed by this server. |
{
"mcpServers": {
"example-1": {
"command": "npx",
"args": ["-y", "tsx"],
"env": {
"ANTHROPIC_API_KEY": "your-value",
"OPENAI_API_KEY": "your-value",
"GOOGLE_API_KEY": "your-value",
"GOOGLE_CLIENT_ID": "your-value",
"GITHUB_CLIENT_ID": "your-value",
"MICROSOFT_CLIENT_ID": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| ANTHROPIC_API_KEY | Credential the server authenticates with. | Yes |
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| GOOGLE_API_KEY | Credential the server authenticates with. | Yes |
| GOOGLE_CLIENT_ID | Configuration value read at startup. | Optional |
| GITHUB_CLIENT_ID | Configuration value read at startup. | Optional |
| MICROSOFT_CLIENT_ID | Configuration value read at startup. | Optional |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.