chat-mcp: A Desktop Chat App that leverages MCP(Model Context Protocol) to interface with other LLMs

by AI-QL · MCP Server · ★ 240

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 76/100

Flagged: sudo usage. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

electronmcpmcp-clientmodel-context-protocol

Quick Facts

Stars240
Forks32
LanguageHTML
CategoryMCP Server
LicenseApache-2.0
Quality Score76/100
Open Issues1
Last Updated2025-09-27
Created2024-12-01
Platformscli, mcp
Est. Tokens~780k

About chat-mcp

MCP Chat Desktop App A Cross-Platform Interface for LLMs This desktop application utilizes the MCP (Model Context Protocol) to seamlessly connect and interact with various Large Language Models (LLMs). Built on Electron, the app ensures full cross-platform compatibility, enabling smooth operation across different operating systems.

chat-mcp alternative? Top 6 similar tools

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    Agentify Desktop lets Codex/Claude/OpenCode control your logged-in ChatGPT, Claude, AiStudio, Gemini, Grok, P

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    Rent the model. Own everything else. Free, open-source AI app for real work.

  • MCPSharp by afrise · ⭐ 377

    MCPSharp is a .NET library that helps you build Model Context Protocol (MCP) servers and clients - the standar

  • flutter-skill by ai-dashboad · ⭐ 362

    AI-powered E2E testing for 10 platforms. 253 MCP tools. Zero config. Works with Claude, Cursor, Windsurf, Copi

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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.

Sources & who's responsible:

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