Short and sweet example MCP server / client implementation for Tools, Resources and Prompts.
Short and sweet example MCP server / client implementation for Tools, Resources and Prompts. The standardizing llm interaction with mcp servers mcp server wraps that behind the Model Context Protocol, so an assistant can use it through 3 defined tools rather than through you.
Model Context Protocol, or MCP, is an open protocol that standardizes how applications provide context to LLMs. In other words it provides a unified framework for LLM based applications to connect to connect to data sources, get context, use tools, and execute standard prompts.
Everything the assistant can do here goes through one of these:
Tools — Tools are functions that the LLM can invoke to perform actions or retrieve information. Each tool is defined with:Resources — Resources represent data sources that can be accessed by the client application. They are identified by URIs and can include:Prompts — Prompts are reusable templates that define specific interaction patterns. They allow servers to expose standardized conversation flows:Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Standardizing LLM Interaction With MCP Servers's toolset — Tools, Resources, Prompts — is a fair guide to whether it matches your workflow. It is maintained by ALucek; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| Tools | Tools are functions that the LLM can invoke to perform actions or retrieve information. Each tool is defined with: |
| Resources | Resources represent data sources that can be accessed by the client application. They are identified by URIs and can include: |
| Prompts | Prompts are reusable templates that define specific interaction patterns. They allow servers to expose standardized conversation flows: |
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.