impart-mcp
Integrates with Vercel's Codex CLI and AI SDK, enabling the MCP server to use Codex agents (e.g., codex/gpt-5.2) for high-difficulty engineering tasks and architecture analysis.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@impart-mcp Research the latest trends in microservices architecture using the researcher agent."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Impart MCP (Agent Orchestration Layer)
impart-mcp 是一个基于 Model Context Protocol (MCP) 的智能体编排服务器。它充当「Agent 编排层」,将具备高度工程化能力的专家智能体(如 Claude Code, Codex CLI 等)封装为标准 MCP 工具,使宿主环境(Claude Desktop, Cursor 等)能够通过统一接口调用这些具备复杂任务处理能力的「超级 Agent」。
🚀 核心特性
多智能体编排:内置 5 种预设专家提示词和工具权限的专业 Agent。
跨平台集成:完美适配 Claude Desktop, Cursor, Gemini CLI 等支持 MCP 的环境。
异步任务处理:支持耗时任务的后台运行与结果轮询。
混合模型供应商:集成 Vercel AI SDK,支持 Claude Code、Codex CLI 和 Gemini CLI 驱动的 Agent。
灵活配置:通过 JSON 配置文件自定义每个 Agent 的模型、开关及参数。
Related MCP server: Agentic Control Framework (ACF)
🛠️ 专家智能体 (Agents)
Agent 名称 | 核心定位 | 默认模型 | 权限 |
Advisor | 架构与调试专家,处理高难度工程问题 |
| Read-only |
Researcher | 代码研究专家,擅长跨仓搜索与文档分析 |
| Read-only |
Explore | 代码定位专家,快速回答「某逻辑在哪里」 |
| Read-only |
Frontend Engineer | UI/UX 实现专家,独立完成高保真组件 |
| Read-write |
Document Writer | 技术文档专家,撰写清晰的规范文档 |
| Read-write |
📦 安装说明
方式一:npx 直接运行(推荐)
无需安装,直接在 MCP 配置中使用:
npx impart-mcp@latest方式二:全局安装
npm install -g impart-mcp
# 或
bun add -g impart-mcp方式三:从源码安装
git clone <repository-url>
cd impart-mcp
bun install # 或 npm install
bun run build # 构建产物🔌 使用集成
配置到 Cursor
打开 Cursor 设置 -> Features -> MCP。
添加新的 MCP Server。
Name:
impart-mcpType:
commandCommand:
npx impart-mcp@latest(或全局安装后直接使用impart-mcp)
配置到 Claude Code
使用 claude mcp add 命令一键添加:
claude mcp add impart-mcp -- npx impart-mcp@latest或全局安装后:
claude mcp add impart-mcp -- impart-mcp⚙️ 配置说明
默认情况下,所有 Agent 使用内置的默认配置即可运行,无需额外配置。
自定义配置(可选)
如需自定义 Agent 的模型或开关,可通过环境变量 AGENT_CONFIG_PATH 指定配置文件路径:
{
"mcpServers": {
"impart-mcp": {
"command": "npx",
"args": ["impart-mcp@latest"],
"env": {
"AGENT_CONFIG_PATH": "/path/to/your/agent-config.json"
}
}
}
}自动初始化:若指定的配置文件不存在,系统会在首次运行时自动生成包含所有默认值的配置模板,方便你按需修改。
配置文件示例 (agent-config.json):
{
"advisor": {
"model": "codex/gpt-5.2",
"enabled": true
},
"researcher": {
"model": "claude/sonnet",
"enabled": true
},
"frontend-ui-ux-engineer": {
"model": "gemini/gemini-3-pro-preview",
"enabled": true
}
}📋 API 文档 (MCP Tools)
服务器提供了以下核心工具:
工具名称 | 类型 | 描述 |
| 同步 | 阻塞式调用,适用于需要立即验证结果的场景 |
| 异步 | 后台启动任务,返回 |
| 查询 | 获取异步任务状态,支持 |
| 批量 | 并行调用多个 Agent,阻塞直到所有任务完成 |
参数说明
call_agent / call_agent_async:
agent:Agent 名称枚举 (advisor|researcher|explore|frontend-ui-ux-engineer|document-writer)prompt:任务描述cwd:工作目录(必填)context:可选上下文信息
👨💻 开发说明
命令 | 描述 |
| 启动服务(Bun 运行时) |
| 开发模式(支持热重载) |
| 构建产物到 |
| 启动服务(Node.js 运行时) |
核心逻辑路径
文件 | 职责 |
| MCP 协议处理与工具注册 |
| Agent 的 System Prompt 及行为定义 |
| 模型供应商适配逻辑 |
| 全局类型定义 |
📄 许可证
Available Tools
4 toolscall_agentA
Synchronous single agent call (BLOCKING, 30-120s).
⚠️ STOP: Choose the Right Tool First
Decision Tree:
Need to run explore/researcher? → Use
call_agent_async(NEVER this tool)Need multiple agents in parallel? → Use
call_agent_async× N, NOTcall_agents_batchNeed advisor advice or must verify result immediately? → Use this tool
Why async for search agents?
explore/researcher may take 30-120s each
Blocking wastes your time waiting
Async lets you continue working while agents search
Available Agents
advisor: Read-only consultation agent. High-IQ reasoning specialist for debugging hard problems and high-difficulty architecture design.
researcher: Specialized codebase understanding agent for multi-repository analysis, searching remote codebases, retrieving official documentation, and finding implementation examples.
explore: Contextual grep for codebases. Answers "Where is X?", "Which file has Y?", "Find the code that does Z".
frontend-ui-ux-engineer: A designer-turned-developer who crafts stunning UI/UX even without design mockups. Code may be a bit messy, but the visual output is always fire.
document-writer: A technical writer who crafts clear, comprehensive documentation. Specializes in README files, API docs, architecture docs, and user guides.
Agent → Tool Mapping
Agent | Tool | Reason |
advisor |
| Need result immediately |
researcher |
| Search task, run in background |
explore |
| Search task, run in background |
frontend-ui-ux-engineer |
| Need result immediately |
document-writer |
| Need result immediately |
Inputs: cwd (required), agent (required), prompt (required), images, +1 more.
call_agent_asyncA
Start agent task in background (NON-BLOCKING). Returns task_id immediately.
✅ Recommended for explore/researcher
Workflow:
call_agent_async→ get task_id instantlyContinue your work (read files, think, etc.)
get_agent_result(task_id, block=true)when ready
Why async is better:
No wasted waiting time
Can launch multiple searches in parallel
Check results when YOU need them
Parallel search pattern:
call_agent_async(explore, "Find X") → task_1
call_agent_async(researcher, "Find Y") → task_2
... do other work ...
get_agent_result(task_1, block=false)
get_agent_result(task_2, block=false)
... do other work ...
get_agent_result(task_1, block=true)
get_agent_result(task_2, block=true)Inputs: cwd (required), agent (required), prompt (required), context.
call_agents_batchA
Execute multiple agents in PARALLEL (BLOCKING until ALL complete).
⚠️ Usually NOT what you want
Prefer call_agent_async × N instead:
Async returns immediately, you can continue working
Batch blocks until the SLOWEST agent finishes
If one agent takes 2min, you wait 2min doing nothing
Only use batch when:
You MUST have ALL results before ANY next step
Results are interdependent (rare)
You're okay blocking for potentially minutes
Example (usually wrong):
{ "calls": [{ "agent": "explore", ... }, { "agent": "researcher", ... }] }↑ This blocks until both finish. Use call_agent_async × 2 instead.
Example (correct use case): Comparing outputs from multiple agents where you need all results simultaneously.
Inputs: cwd (required), calls (required).
get_agent_resultA
Get result from async agent task.
Parameters:
task_id: The task ID from call_agent_async
block: If true, wait for completion (default: false)
timeout: Max wait time in ms when blocking (default: 300000 = 5min)
Returns task status and result if completed.
Inputs: task_id (required), block, timeout.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 4 tools
The tools are functionally distinct by execution model (sync, async, batch, result retrieval), but call_agent_async and call_agents_batch overlap in parallel execution capability, requiring extensive warnings to steer usage. The descriptions clarify intent, but the ambiguity is notable enough to deduct a point.
All tools follow a snake_case verb_noun pattern with clear prefixes (call_, get_). However, the distinction between call_agent, call_agent_async, and call_agents_batch is subtle, and pluralization is inconsistent, slightly reducing predictability.
Four tools is an appropriate scope for an agent orchestration server, covering the essential sync, async, batch, and result retrieval patterns without bloat or redundancy. Each tool serves a distinct purpose in the workflow.
The surface covers the core lifecycle of launching and retrieving agent results, including synchronous and asynchronous execution. Missing cancellation or task listing is a minor gap, but the main workflows are well-supported.
Maintenance
Related MCP Connectors
One MCP endpoint for Claude, GPT & Gemini: 100+ tools + no-code connectors + agent workers.
LLM Orchestration Agent (Mcp)
MCP server for your apps' tools and custom tools, plus hosted AI agents and approval-gated workflows
Build and manage AI-native customer support agents from Claude or any MCP client.
Related MCP Servers
- FlicenseBqualityDmaintenanceA Model Context Protocol server that enables Claude users to access specialized OpenAI agents (web search, file search, computer actions) and a multi-agent orchestrator through the MCP protocol.410-
- AlicenseDqualityDmaintenanceAI-native orchestration layer with 80+ tools for task management, code editing, browser automation, terminal control, and persistent memory across CLI, local MCP, and cloud deployments.694 npm31ISC
- AlicenseBqualityBmaintenanceAn MCP orchestration layer that aggregates multiple MCP servers while exposing only 8 meta-tools, dramatically reducing context window usage, and provides SLOP scripting, event monitoring, and tool customization.101MIT
- AlicenseBqualityFmaintenanceEnables orchestrating multiple AI CLI agents (Claude Code, Codex, Gemini CLI, Copilot CLI) through a unified MCP interface for task delegation, cross-agent comparison, and specialized tools like code review and debugging.149 npm14MIT