A CLI host application that enables Large Language Models (LLMs) to interact with external tools through the Model Context Protocol (MCP). Currently
A CLI host application that enables Large Language Models (LLMs) to interact with external tools through the Model Context Protocol (MCP). Currently supports Openai, Azure Openai, Deepseek and Ollama models. Exposed over MCP by the mcp cli host mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
mcp-cli-host on PyPI is all you need. Most clients run it directly, so configuration is a few lines and a restart.
Everything the assistant can do here goes through one of these:
Examples — The Examples tool exposed by this serverFlags — The Flags tool exposed by this serverYou will need 6 environment variables: OPENAI_API_KEY, AZURE_OPENAI_DEPLOYMENT, AZURE_OPENAI_API_KEY, AZURE_OPENAI_API_VERSION, AZURE_OPENAI_ENDPOINT, GEMINI_API_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
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. MCP Cli Host's toolset — Examples, Flags — is a fair guide to whether it matches your workflow. It is maintained by vincent-pli; 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 |
|---|---|
| Examples | The Examples tool exposed by this server. |
| Flags | The Flags tool exposed by this server. |
{
"mcpServers": {
"cli-host": {
"command": "uvx",
"args": ["mcp-cli-host"],
"env": {
"OPENAI_API_KEY": "your-value",
"AZURE_OPENAI_DEPLOYMENT": "your-value",
"AZURE_OPENAI_API_KEY": "your-value",
"AZURE_OPENAI_API_VERSION": "your-value",
"AZURE_OPENAI_ENDPOINT": "your-value",
"GEMINI_API_KEY": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| AZURE_OPENAI_DEPLOYMENT | Configuration value read at startup. | Optional |
| AZURE_OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| AZURE_OPENAI_API_VERSION | Configuration value read at startup. | Optional |
| AZURE_OPENAI_ENDPOINT | Configuration value read at startup. | Optional |
| GEMINI_API_KEY | Credential the server authenticates with. | Yes |
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.