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Qwen MCP Server

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Qwen MCP Server

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

MCP Badge License: MIT Zero Config Node MCP

A Model Context Protocol server that exposes the canonical Qwen knowledge surface — models, prompts, and chat workflows, pricing, FAQ, official links — 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

💬 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 — code, documents, visual inputs, and web generation — 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.

Related MCP server: Model Hub MCP

Key Features

  • 1M context window — carry multiple long files, research documents, or codebases in a single request without manually summarising between steps.

  • Model lane comparison — 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 — the underlying models expose both API surfaces, so existing tooling, SDKs, and prompt templates migrate with minimal changes.

  • Benchmark transparency — 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 — the chat accepts screenshots, PDFs, and diagrams, enabling visual reasoning tasks such as UI analysis, screen grounding, and document layout parsing.

  • Preset task examples — 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 — 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 — uploading policy documents, research papers, or technical specifications and asking structured questions without hitting context limits.

  • Visual and UI tasks — pasting screenshots for layout analysis, accessibility review, or screen grounding where the model identifies interactive elements by position.

  • Rapid web prototyping — generating functional HTML or component scaffolding from a brief description and iterating in the same session.

  • API migration evaluation — 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 — 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 — Supported chat models and capability notes.

  • site://chat/pricing — Canonical pricing entry point.

  • site://chat/faq — Short FAQ generated from public site metadata.

  • site://chat/links — Canonical URLs to share with users.

Prompts

tell_me_about_chat

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

start_chat_session_chat

Open a chat-evaluation session against the site's models, with sensible defaults. — 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

Available Tools

3 tools
get_pricingB

Return the canonical pricing entry point for Qwen.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so description must fully disclose behavior. It only states that it returns an entry point, lacking details on authentication, error conditions, or side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence is concise and front-loaded, but could benefit from additional context like what the pricing entry point is.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Lacks details on return type or content; with no output schema, agent cannot infer what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters in input schema, so description need not compensate. Baseline score of 4 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description uses verb 'Return' and specifies resource 'canonical pricing entry point for Qwen', distinguishing from sibling tools like list_models and get_official_links. However, 'entry point' is somewhat vague; it could be a URL or a document.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus sibling tools; the description does not mention any preconditions or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_modelsA

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

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A3.8/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description bears the full burden of behavioral disclosure. It indicates a read-only operation returning a list, but lacks details on caching, error behavior, or output constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence that conveys the essential information without redundancy. It is well-structured and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters, no output schema, and sibling tools covering pricing and links, the description sufficiently explains the tool's purpose. However, it could mention if models include metadata or if the list is exhaustive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero parameters, so the description does not need to elaborate on them. It adds value by noting 'capability notes', clarifying the output content. Baseline for 0 params is 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns the canonical list of chat models with capability notes, distinguishing it from sibling tools like get_pricing and get_official_links which deal with pricing and links respectively.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool versus its siblings. While the tool names provide some implicit context, no direct guidance on usage or exclusion is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updatesv0.1.0
    • First observedget_official_links
    • First observedget_pricing
    • First observedlist_models
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TDQS

A3.6/5.0

Scored across 3 tools

Disambiguation5/5

Each tool serves a distinct purpose: listing models, getting pricing, and obtaining official links. There is no functional overlap, making it easy for an agent to select the correct tool.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (list_models, get_pricing, get_official_links), which is predictable and aids in understanding.

Tool Count4/5

With 3 tools, the server is lean but not too sparse for its informational purpose. The count seems appropriate for providing canonical lists and pricing without being overwhelming.

Completeness3/5

The tools cover basic informational needs (models, pricing, links) but lack operations like model details or interactive features. The surface is complete for a simple reference server, but minor gaps exist for advanced use cases.

Resources

Unclaimed servers have limited discoverability.

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