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Claude Team MCP
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Claude Team MCP

Claude Team is a multi - agent MCP server that enables intelligent task distribution, preset workflow templates, and a custom expert system by configuring multiple AI models (such as GPT, Claude, Gemini) to work together, for automated collaboration on complex development tasks.
Developer toolsArtificial intelligence chatbots#Multi - agent collaboration#AI development team#Task automation#MCP service.TypeScript
2.5 points
6.1K
Open Site

Overview

Installation

Content Details

Alternatives

What is Claude Team?

Claude Team is an intelligent collaboration server based on the Model Context Protocol (MCP). It can organize multiple different AI models (such as GPT-4, Claude, Gemini, etc.) into an efficient development team. Just like a real - world development team with members of different specialties, Claude Team can automatically assign tasks to the most suitable AI experts, enabling them to collaborate on complex tasks such as code generation, bug fixing, and code review.

How to use Claude Team?

Using Claude Team is very simple: First, install it globally via npm or configure the MCP server in your IDE. Then, set the API keys and model configurations. After the configuration is complete, you can directly use natural language to communicate with the AI team in supported IDEs (such as Claude Code, Windsurf, Cursor). For example, say 'Help me optimize this SQL query' or 'Build a user login function', and the team will automatically assign tasks and collaborate to complete them.

Use cases

Claude Team is particularly suitable for complex development tasks that require multi - angle thinking: 1. Full - stack project development - Front - end, back - end, and testing experts work together 2. Code optimization and refactoring - Performance, security, and code quality experts conduct joint reviews 3. Technical solution design - Architects and domain experts collaborate on design 4. Documentation generation and maintenance - Technical documentation experts and code experts cooperate 5. Emergency bug fixing - Diagnosis, repair, and verification experts respond quickly

Main features

๐Ÿค– Multi - model collaboration
Supports configuring multiple AI models to work together, with each model leveraging its expertise. For example, GPT - 4 can handle complex reasoning, Claude can handle code generation, and Gemini can handle documentation writing.
๐Ÿง  Smart task distribution
The built - in 'technical leader' will automatically analyze task requirements and assign them to the most suitable experts. The system will make optimal assignments based on task complexity, expert specialties, and model capabilities.
๐Ÿ”— Workflow templates
Provides 5 predefined workflows: code generation, bug fixing, code refactoring, code review, and documentation generation. Each workflow has optimized collaboration steps.
๐ŸŽฏ Custom experts
In addition to the built - in front - end, back - end, and QA experts, you can define your own experts via environment variables, such as Rust experts, K8s experts, and security experts.
๐Ÿ“Š Observability
Provides functions such as a team dashboard, cost estimation, and task plan preview, allowing you to clearly understand the team's working status, resource consumption, and progress.
๐ŸŒ Proxy API support
Supports customizing the API base URL and is compatible with various proxy services, facilitating use in enterprise intranets or specific environments.
๐Ÿ“ Collaboration history
Completely records all collaboration sessions, supports searching and viewing historical records, facilitating review and knowledge management.
Advantages
Intelligent collaboration: Multiple AI models work together like a real team, providing a more comprehensive solution than a single model
Cost optimization: Different levels of models can be selected according to task complexity, balancing effectiveness and cost
Flexible configuration: Supports customizing experts and workflows to meet different project requirements
Easy integration: As an MCP server, it can be easily integrated into mainstream IDEs
Transparent and controllable: Provides complete observability to understand the processing process and cost of each task
Limitations
Configuration complexity: Requires configuring multiple API keys and model parameters, with a certain learning curve for initial setup
API cost: Using multiple models may increase API call costs, requiring reasonable planning
Response time: Team collaboration requires multiple models to process sequentially or in parallel, which may be slower than a single model
Network dependency: Requires a stable network connection to call the APIs of various AI providers
Model differences: The performance of different models may vary, requiring configuration tuning

How to use

Install Claude Team
Install it globally via npm or run it directly using npx. It is recommended to use the npx method without permanent installation.
Configure the MCP settings in your IDE
According to the IDE you are using, add the Claude Team server configuration to the corresponding configuration file. You need to set environment variables such as API keys and models.
Configure environment variables
Set the necessary environment variables, including the API key, URL, model ID, and provider of the main model. You can configure multiple working models.
Restart your IDE and start using
Restart your IDE to load the new MCP configuration. Then, you can use various tools of Claude Team in the chat interface.

Usage examples

Case 1: Optimize the performance of an SQL query
The user has a complex SQL query that needs performance optimization. The technical leader of Claude Team will analyze the task, create SQL optimization experts and index analysis experts, and use a sequential workflow to optimize step by step.
Case 2: Build a settings page
The user needs to build a settings page with a dark mode switch. The team will automatically create UI component experts, theme system experts, and state management experts, work in parallel, and then have the technical leader review.
Case 3: Code security review
The user needs to review the security of a piece of code. The team will start security experts, code quality experts, and performance experts in parallel to conduct reviews from different angles.
Case 4: Generate technical documentation
The user needs to generate API documentation for an existing codebase. The team will analyze the code structure and then have documentation experts and technical experts collaborate to generate the documentation.

Frequently Asked Questions

How many API keys does Claude Team require?
Which AI model providers are supported?
How to control usage costs?
Can experts be customized?
Where are the team collaboration historical records saved?
What if the API call of a certain model fails?
Which IDEs are supported?
How to update Claude Team?

Related resources

GitHub repository
Source code and the latest version of Claude Team
npm package page
Package information and installation instructions on npm
Model Context Protocol official website
Official documentation and specifications of the MCP protocol
Problem feedback and discussion
Submit bug reports, feature requests, or participate in discussions
Contribution guide
How to contribute code to Claude Team

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "claude-team": {
      "command": "npx",
      "args": ["-y", "claude-team"],
      "env": {
        "CLAUDE_TEAM_MAIN_KEY": "sk-your-api-key",
        "CLAUDE_TEAM_MAIN_URL": "https://api.openai.com/v1",
        "CLAUDE_TEAM_MAIN_MODEL": "gpt-4o",
        "CLAUDE_TEAM_MAIN_PROVIDER": "openai"
      }
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

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