<i>A simple yet powerful Python client for interacting with Model Context Protocol (MCP) servers using Ollama, allowing you to harness local LLMs for
Most AI and media services work still happens through a UI a human drives. MCP Client For Ollama MCP server moves it into the conversation instead. A simple yet powerful Python client for interacting with Model Context Protocol (MCP) servers using Ollama, allowing you to harness local LLMs for advanced tool execution..
MCP Client for Ollama (ollmcp) is a modern, interactive terminal application (TUI) built for harness engineering, connecting local Ollama LLMs to one or more Model Context Protocol (MCP) servers. By fully supporting the core MCP primitives (tools, prompts, and resources), it provides a controlled terminal space where you steer, and the agent executes. With a rich, user-friendly interface, it lets you safely manage your setup in real time with no coding required. Whether you're building, testing, or exploring, this client streamlines your workflow with features like fuzzy autocomplete, advanced model
The server publishes 1 tool. What each one is for:
Scopes — The project scope writes a standard .mcp.json file at your project root, compatible with Claude Code and other MCP-aware tools. If the same serverConfiguration is passed through the environment: YOUR_KEY, OLLMCP_API_KEY, ATLASCLOUD_API_KEY, AZURE_OPENAI_API_KEY, DASHSCOPE_API_KEY, DATABRICKS_TOKEN, DEEPINFRA_API_KEY, DEEPSEEK_API_KEY. 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.
ollama list to see available models. If no models are installed, you can pull one using ollama pull <model_name>. For example, ollama pull gemma4:latest. - UV package manager ([InstallationBecause this one is hosted, setup is mostly authentication — you point your client at the endpoint and approve access. Nothing runs on your machine, so there is no runtime to keep patched.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. MCP Client For Ollama's toolset — Scopes — is a fair guide to whether it matches your workflow. It is maintained by jonigl; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against MCP Client For Ollama's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| Scopes | The project scope writes a standard .mcp.json file at your project root, compatible with Claude Code and other MCP-aware tools. If the same server name exists in multiple scopes, precedence is local > project > user. |
{
"mcpServers": {
"client-for-ollama": {
"command": "npx",
"args": ["-y", "@playwright/mcp"],
"env": {
"YOUR_KEY": "your-value",
"OLLMCP_API_KEY": "your-value",
"ATLASCLOUD_API_KEY": "your-value",
"AZURE_OPENAI_API_KEY": "your-value",
"DASHSCOPE_API_KEY": "your-value",
"DATABRICKS_TOKEN": "your-value",
"DEEPINFRA_API_KEY": "your-value",
"DEEPSEEK_API_KEY": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
ollama list to see available models. If no models are installed, you can pull one using ollama pull <model_name>. For example, ollama pull gemma4:latest. - UV package manager ([Installation| Variable | Description | Required |
|---|---|---|
| YOUR_KEY | Credential the server authenticates with. | Yes |
| OLLMCP_API_KEY | Credential the server authenticates with. | Yes |
| ATLASCLOUD_API_KEY | Credential the server authenticates with. | Yes |
| AZURE_OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| DASHSCOPE_API_KEY | Credential the server authenticates with. | Yes |
| DATABRICKS_TOKEN | Credential the server authenticates with. | Yes |
| DEEPINFRA_API_KEY | Credential the server authenticates with. | Yes |
| DEEPSEEK_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.