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即梦AI图像生成服务 MCP Setup & Guide

MCP ServerMCP Hub 中国

io.mcp-cn.jimeng-mcp

即梦AI图像生成服务的MCP服务器，支持即梦AI图像生成服务

View on MCP Hub 中国 ↗Repository ↗

Copy the install config on this page first, then verify docs and permissions upstream.

Popularity

Uses18.2k

Overview

即梦AI图像生成服务的MCP服务器，支持即梦AI图像生成服务 即梦AI图像生成服务 is a MCP Server listed from MCP Hub China. Transports: stdio. This page includes an overview, setup tutorial, install commands, and use cases for Trae, Tongyi Lingma, Cursor, Claude Code, and VS Code.

即梦AI图像生成服务MCPAI

Use cases

China services

AgentHub Verified AvailabilityTested & Ready

Automated pipeline validated install commands, protocol & client compatibility

Verified At2026-09-29
Install Snippets TestedCLI & JSON config syntax verified
Protocol Handshake ReadyComplies with JSON-RPC 2.0 specifications
Origin Registry ActiveSourced from mcp-cn
Verified ClientsClaude Code、Claude Desktop、Cursor 等
Security Tier: A 级 · 源码开放合规 (A)·Scanned for high-risk vulnerabilities. Recommended to run with project-scoped permissions.

Copy by platform

Choose your platform

  1. Open or create .cursor/mcp.json in your project root
  2. Click Copy config and paste; merge only this mcpServers entry if others exist
  3. Replace <placeholders> in env with real secrets (see Environment variables below)
  4. Save, then Cmd+Shift+P → Reload Window

Pre-fill Environment Variables (Optional)

Requiredsecret
Privacy Guarantee: Keys are replaced 100% locally in your browser memory and are NEVER uploaded or stored on any server.
Click to copy snippet
{
  "mcpServers": {
    "io-mcp-cn-jimeng-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "jimeng-mcp"
      ],
      "env": {
        "JIMENG_API_TOKEN": "<JIMENG_API_TOKEN>"
      }
    }
  }
}

Setup tutorial

  1. Open the 即梦AI图像生成服务 page and confirm this MCP Server (source: MCP Hub China).
  2. Copy the Cursor, Claude Code, or VS Code snippet.
  3. Merge it into mcpServers and replace env placeholders with real secrets.
  4. Reload the window, then call the MCP tools from your agent chat.

Install commands

Install commands and setup steps are in the HTML so search engines and no-JS browsers can read them without running client JavaScript.

Claude Code (local)

  1. Install Claude Code CLI
  2. Copy the command below, replace <placeholders> with real env values, then run in terminal
  3. See Environment variables below if listed
claude mcp add io-mcp-cn-jimeng-mcp --env JIMENG_API_TOKEN=<JIMENG_API_TOKEN> -- npx -y jimeng-mcp

Cursor — .cursor/mcp.json (local)

  1. Open or create .cursor/mcp.json in your project root
  2. Click Copy config and paste; merge only this mcpServers entry if others exist
  3. Replace <placeholders> in env with real secrets (see Environment variables below)
  4. Save, then Cmd+Shift+P → Reload Window
{
  "mcpServers": {
    "io-mcp-cn-jimeng-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "jimeng-mcp"
      ],
      "env": {
        "JIMENG_API_TOKEN": "<JIMENG_API_TOKEN>"
      }
    }
  }
}

VS Code — .vscode/mcp.json (local)

  1. Install GitHub Copilot in VS Code with MCP support
  2. Open or create .vscode/mcp.json in your project root
  3. Click Copy config and paste; merge only this mcpServers entry if others exist
  4. Replace <placeholders> in env with real secrets
  5. Save and Developer: Reload Window
{
  "mcpServers": {
    "io-mcp-cn-jimeng-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "jimeng-mcp"
      ],
      "env": {
        "JIMENG_API_TOKEN": "<JIMENG_API_TOKEN>"
      }
    }
  }
}

Claude Desktop — claude_desktop_config.json (local)

  1. Open Claude Desktop claude_desktop_config.json (see remote guide for paths)
  2. Click Copy config and merge under mcpServers
  3. Replace <placeholders> in env with real secrets
  4. Fully quit and restart Claude Desktop
{
  "mcpServers": {
    "io-mcp-cn-jimeng-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "jimeng-mcp"
      ],
      "env": {
        "JIMENG_API_TOKEN": "<JIMENG_API_TOKEN>"
      }
    }
  }
}

Trae — .trae/mcp.json (local)

  1. Trae → Settings → MCP, or edit .trae/mcp.json / global mcp.json
  2. Click Copy config and merge mcpServers
  3. Replace <placeholders> in env with real secrets
  4. Save and reload Trae
{
  "mcpServers": {
    "io-mcp-cn-jimeng-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "jimeng-mcp"
      ],
      "env": {
        "JIMENG_API_TOKEN": "<JIMENG_API_TOKEN>"
      }
    }
  }
}

Cherry Studio — MCP settings (local)

  1. Cherry Studio → Settings → MCP Servers → Add (STDIO)
  2. Or import JSON: click Copy config and merge mcpServers
  3. Replace <placeholders> in env; ensure Node.js / uv (npx, uvx) are installed
  4. Enable the server and check tools load
{
  "mcpServers": {
    "io-mcp-cn-jimeng-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "jimeng-mcp"
      ],
      "env": {
        "JIMENG_API_TOKEN": "<JIMENG_API_TOKEN>"
      }
    }
  }
}

Tongyi Lingma — MCP config (local)

  1. Lingma Settings → MCP → + → STDIO or config file
  2. Click Copy config and merge mcpServers
  3. Replace <placeholders> in env; need Node.js 18+ (npx) or uv (uvx)
  4. Confirm connected before using tools in agent chat
{
  "mcpServers": {
    "io-mcp-cn-jimeng-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "jimeng-mcp"
      ],
      "env": {
        "JIMENG_API_TOKEN": "<JIMENG_API_TOKEN>"
      }
    }
  }
}

Windsurf — mcp_config.json (local)

  1. Edit ~/.codeium/windsurf/mcp_config.json
  2. Click Copy config and merge mcpServers (stdio same as Cursor)
  3. Replace <placeholders> in env, save, refresh Cascade
{
  "mcpServers": {
    "io-mcp-cn-jimeng-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "jimeng-mcp"
      ],
      "env": {
        "JIMENG_API_TOKEN": "<JIMENG_API_TOKEN>"
      }
    }
  }
}

Cline — MCP Servers (local)

  1. Cline panel → Settings → MCP Servers
  2. Click Copy config and merge
  3. Replace <placeholders> in env, then save
{
  "mcpServers": {
    "io-mcp-cn-jimeng-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "jimeng-mcp"
      ],
      "env": {
        "JIMENG_API_TOKEN": "<JIMENG_API_TOKEN>"
      }
    }
  }
}

WorkBuddy — .workbuddy/mcp.json (local)

  1. Edit ~/.workbuddy/mcp.json (user) or project .workbuddy/mcp.json
  2. Or Plugins → MCP Servers → Configure MCP and paste the JSON below
  3. Replace <placeholders> in env; on Windows prefer absolute paths for command/scripts
  4. Save, restart WorkBuddy, confirm connector status is green
{
  "mcpServers": {
    "io-mcp-cn-jimeng-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "jimeng-mcp"
      ],
      "env": {
        "JIMENG_API_TOKEN": "<JIMENG_API_TOKEN>"
      }
    }
  }
}

Troubleshooting & Common ErrorsFAQ

Common connection errors and verified fixes for 即梦AI图像生成服务

Getting 'connection closed' or exit code 1 in Cursor / Claude Code for 即梦AI图像生成服务?

Usually caused by missing runtime paths or IDE environment inheritance. Fix steps: 1. Verify Node.js 18+ (npx) or Python 3.10+ (uvx) is installed; 2. Run 'which npx' or 'which uvx' in terminal, and replace 'command' with absolute path; 3. Reload or restart the client window after modifying config.

Fix Snippet
# Check binary path in terminal:
which npx
node -v
Getting 'spawn npx ENOENT' or 'command not found'?

The editor background process does not inherit your full terminal PATH. Solution: Globally install the package, or set the absolute binary path (e.g. C:\Program Files\nodejs\npx.cmd on Windows, or /usr/local/bin/npx on macOS).

Missing required environment variable or authentication failure?

即梦AI图像生成服务 requires environment variables (JIMENG_API_TOKEN). Ensure you have added valid keys under the 'env' object in your config file without trailing whitespace.

Fix Snippet
// .cursor/mcp.json 或 claude_desktop_config.json
{
  "env": {
    "JIMENG_API_TOKEN": "your_actual_key_here"
  }
}
Still not connecting? Read the MCP troubleshooting guide →

Tool Mock Playground

Sandbox

即梦AI图像生成服务 Tool Interface Simulation · Preview tool schema & outputs without local runtime

Sandbox Ready
fn: io_mcp_cn_jimeng_mcpExecute the core tool interface of 即梦AI图像生成服务
Request ArgumentsJSON Schema
{
  "target": "即梦AI图像生成服务",
  "action": "execute",
  "options": {
    "mode": "standard",
    "timeoutMs": 5000
  }
}
💡Parameters generated dynamically by Agent runtime
Agent Tool Output

Click 'Run Mock' above

to preview the raw response returned to the LLM

Env: AgentHub Virtual SandboxJSON-RPC 2.0

Decision Guide: Why & When to Use

Assess suitability before installing to save trial-and-error time

Best Suited For
  • Feishu docs
  • China maps routing
  • WeCom notifications
When NOT to Use
  • Teams with no China accounts
  • Cross-border data compliance concerns
Recommended Workflow Pairing:View Scenario →

即梦AI图像生成服务 + Scenario Prompt → Complete Agent Automation

MCP hands-on: install to visible results

Follow the full lab (expected UI/output + contrast checks). After installing this item, verify with the tutorial prompts.

Open tutorial →

Related resources

Often paired with

Keep exploring AgentHub

Most people compare similar tools or check scenario guides before installing—start here.

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Copy either snippet into your project homepage, docs, or GitHub README. The badge is a hotlinked SVG — nothing to host — and it links back to this page so visitors can find the install steps.

PreviewListed on AgentHub: 即梦AI图像生成服务
HTML
<a href="https://myagenthub.cn/p/io.mcp-cn.jimeng-mcp" title="Listed on AgentHub: 即梦AI图像生成服务" target="_blank" rel="noopener">
  <img src="https://myagenthub.cn/badge/io.mcp-cn.jimeng-mcp?lang=en" alt="Listed on AgentHub: 即梦AI图像生成服务" height="20" style="border:0"/>
</a>
Markdown (GitHub README)
[![Listed on AgentHub: 即梦AI图像生成服务](https://myagenthub.cn/badge/io.mcp-cn.jimeng-mcp?lang=en)](https://myagenthub.cn/p/io.mcp-cn.jimeng-mcp)

Badges are generated on the fly from /badge/<package-id>, so name and listing changes propagate automatically. Keep the link target unchanged — it is what counts as the referral.

Unified Manifest

{
  "id": "io.mcp-cn.jimeng-mcp",
  "type": "mcp-server",
  "version": "latest",
  "displayName": "即梦AI图像生成服务",
  "description": "即梦AI图像生成服务的MCP服务器，支持即梦AI图像生成服务",
  "author": {
    "name": "rick"
  },
  "iconUrl": "https://avatars.githubusercontent.com/u/17995300?v=4",
  "repository": {
    "url": "https://github.com/c-rick/jimeng-mcp",
    "source": "github"
  },
  "homepage": "https://mcp-cn.com/server/111",
  "distribution": {
    "packages": [
      {
        "registryType": "npm",
        "identifier": "jimeng-mcp",
        "runtimeHint": "npx",
        "transport": "stdio",
        "environmentVariables": [
          {
            "name": "JIMENG_API_TOKEN",
            "description": "<your-api-token>",
            "isRequired": true,
            "isSecret": true
          }
        ]
      }
    ],
    "remotes": []
  },
  "dependencies": [],
  "installTargets": [
    "claude-code",
    "claude-desktop",
    "cursor",
    "vscode",
    "trae",
    "cherry-studio",
    "lingma",
    "windsurf",
    "cline",
    "workbuddy"
  ],
  "keywords": [
    "use_count:18204",
    "即梦AI图像生成服务",
    "MCP",
    "即梦AI图像生成服务",
    "AI"
  ],
  "provenance": {
    "origin": "mcp-cn",
    "originalId": "jimeng-mcp",
    "originalUrl": "https://mcp-cn.com/server/111",
    "isOfficial": false,
    "status": "active"
  }
}
即梦AI图像生成服务 MCP Setup & Guide - AgentHub