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Chat MCP Server

Developer ToolsModerate7.2MCP RegistryLocal
Free

Server data from the Official MCP Registry

Qwen AI Chat guide to Qwen3.6-Plus and qwen3.6-flash with practical notes on 1M context, coding

About

Qwen AI Chat guide to Qwen3.6-Plus and qwen3.6-flash with practical notes on 1M context, coding

Security Report

7.2
Moderate7.2Low Risk

Excellent MCP server with clean, focused design. The server is read-only, requires no authentication or API keys, and exposes only static informational content about Qwen Chat. Code is well-structured, has no credential handling, no external API calls, no dangerous operations, and permissions are minimal and appropriate for the stated purpose. Supply chain analysis found 2 known vulnerabilities in dependencies (0 critical, 2 high severity).

4 files analyzed ยท 2 issues found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

env_vars

Check that this permission is expected for this type of plugin.

How to Install

Add this to your MCP configuration file:

{
  "mcpServers": {
    "io-github-rocnubie-chat-mcp": {
      "args": [
        "-y",
        "msa-chat-mcp"
      ],
      "command": "npx"
    }
  }
}

Documentation

View on GitHub

From the project's GitHub README.

Qwen MCP Server

Qwen AI Chat Guide for Qwen3.6-Plus, Flash, and 1M Context

MCP Badge License: MIT Zero Config Node MCP smithery

A Model Context Protocol server that exposes the canonical Qwen knowledge surface โ€” models, prompts, and chat workflows, pricing, FAQ, official links โ€” to MCP-compatible AI clients such as Claude Desktop, Cursor, Windsurf, and Continue. Read-only, no API keys, no quota, ~50 ms cold start.

Official website: https://qwenchat.online

๐Ÿ’ฌ About Qwen

Qwen Chat (qwenchat.online) is a browser-based workspace for interacting with Alibaba's Qwen3 family of language models. The site provides direct chat access alongside a structured evaluation environment where users can test model capabilities across distinct task categories โ€” code, documents, visual inputs, and web generation โ€” without setting up local infrastructure. It is designed for people who want to move quickly from curiosity to practical assessment: load a file, paste a prompt, and observe how the model handles real work rather than toy examples.

Key Features

  • 1M context window โ€” carry multiple long files, research documents, or codebases in a single request without manually summarising between steps.
  • Model lane comparison โ€” switch between Qwen3 Plus and Flash variants through the same interface to compare speed and quality tradeoffs on the same input.
  • OpenAI and Anthropic API compatibility โ€” the underlying models expose both API surfaces, so existing tooling, SDKs, and prompt templates migrate with minimal changes.
  • Benchmark transparency โ€” the site surfaces specific third-party scores (Terminal-Bench 2.0, OmniDocBench 1.5) rather than abstract capability claims, letting users calibrate expectations before committing to integration.
  • Multimodal input support โ€” the chat accepts screenshots, PDFs, and diagrams, enabling visual reasoning tasks such as UI analysis, screen grounding, and document layout parsing.
  • Preset task examples โ€” the interface ships with ready-made prompts covering code review, document analysis, and web prototyping, reducing the setup time needed to start a meaningful test.

Use Cases

  • Code and repository work โ€” reviewing diffs, diagnosing build failures, or walking through a repository repair task using the extended context window to hold multiple files at once.
  • Long-document analysis โ€” uploading policy documents, research papers, or technical specifications and asking structured questions without hitting context limits.
  • Visual and UI tasks โ€” pasting screenshots for layout analysis, accessibility review, or screen grounding where the model identifies interactive elements by position.
  • Rapid web prototyping โ€” generating functional HTML or component scaffolding from a brief description and iterating in the same session.
  • API migration evaluation โ€” testing whether Qwen models can serve as a drop-in replacement for OpenAI or Anthropic endpoints in an existing workflow before switching production traffic.

Who Is It For

Qwen Chat is aimed primarily at developers, technical leads, and product teams who are already using language models in their day-to-day work and want to evaluate whether Qwen3 fits their stack. It suits teams that handle long documents or large codebases and need a context window beyond what standard tiers offer. It is also relevant to engineers assessing API compatibility before committing to a migration โ€” the site provides enough structured evidence to make that call without requiring a full integration first. Individual developers building side projects or prototypes who want a capable model accessible directly from the browser will find it practical as well.

Tools

list_models

Return the canonical list of chat models exposed on the site, with capability notes. (Qwen)

Input: no parameters. Returns: text/markdown.

get_pricing

Return the canonical pricing entry point for Qwen.

Input: no parameters. Returns: text/markdown.

get_official_links

Return the canonical list of official links for Qwen (website, support, docs when available).

Input: no parameters. Returns: text/markdown.

Resources

  • site://chat/models โ€” Supported chat models and capability notes.
  • site://chat/pricing โ€” Canonical pricing entry point.
  • site://chat/faq โ€” Short FAQ generated from public site metadata.
  • site://chat/links โ€” Canonical URLs to share with users.

Prompts

tell_me_about_chat

Summarize what the site is, who it's for, and how it works. โ€” Qwen

start_chat_session_chat

Open a chat-evaluation session against the site's models, with sensible defaults. โ€” Qwen

Installation

Install via Smithery

npx -y @smithery/cli install chat-mcp --client claude

(Replace claude with cursor, windsurf, or continue for those clients.)

Install from source

git clone https://github.com/rocnubie/chat-mcp.git
cd chat-mcp
pnpm install

Then add to your MCP client config (claude_desktop_config.json for Claude Desktop, mcp.json for Cursor / Windsurf / Continue):

{
  "mcpServers": {
    "chat-mcp": {
      "command": "node",
      "args": [
        "/absolute/path/to/chat-mcp/src/index.mjs"
      ]
    }
  }
}

Debug with MCP Inspector

npx @modelcontextprotocol/inspector node src/index.mjs

Official Links

Development

pnpm install
pnpm start                 # run the server over stdio

License

MIT

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