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by adhikasp · MCP Server · ★ 677
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MCP CLI client A simple CLI program to run LLM prompt and implement Model Context Protocol (MCP) client. You can use any MCP-compatible servers from the convenience of your terminal. This act as alternative client beside Claude Desktop. Additionally you can use any LLM provider like OpenAI, Groq, or local LLM model via llama. Setup Install via pip: Create a file to configure your LLM and MCP servers: json { "systemPrompt": "You are an AI assistant helping a software engineer...", "llm": { "provider": "openai", "model": "gpt-4", "apikey": "your-openai-api-key", "temperature": 0.7, "baseurl": "https://api.openai.com/v1" // Optional, for OpenRouter or other providers }, "mcpServers": { "fetch": { "command": "uvx", "args": ["mcp-server-fetch"], "requiresconfirmation": ["fetch"], "enabled": true, // Optional, defaults to true "excludetools": [] // Optional, list of tool names to exclude }, "brave-search": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-brave-search"], "env": {
| Stars | 677 |
| Forks | 84 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 74.6769430896026/100 |
| Open Issues | 22 |
| Last Updated | 2025-12-02 |
| Created | 2024-11-27 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~19k |
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mcp-client-cli is A simple CLI to run LLM prompt and implement MCP client.. It is categorized as a MCP Server with 677 GitHub stars.
mcp-client-cli is primarily written in Python. It covers topics such as langchain, llm, mcp.
You can find installation instructions and usage details in the mcp-client-cli GitHub repository at github.com/adhikasp/mcp-client-cli. The project has 677 stars and 84 forks, indicating an active community.
mcp-client-cli is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mcp-client-cli on Agent Skills Hub include oreilly-ai-agents, jadx-ai-mcp, generative-ai. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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