Schema | Claude Team MCP

Claude Team MCP

Describes the environment variables required to run the server.

Name	Required	Description	Default
`GEMINI_API_KEY`	No	Gemini API Key for fast tasks (optional)
`OPENAI_API_KEY`	No	OpenAI API Key for balanced tasks (optional)
`ANTHROPIC_API_KEY`	No	Anthropic API Key for powerful tasks (optional)
`CLAUDE_TEAM_MAIN_KEY`	Yes	主模型 API Key (required)
`CLAUDE_TEAM_MAIN_URL`	No	主模型 API 地址 (optional)
`CLAUDE_TEAM_MAIN_MODEL`	No	主模型 ID (optional, default: gpt-4o)	gpt-4o
`CLAUDE_TEAM_MODEL1_KEY`	No	模型1 API Key (optional, defaults to MAIN key)
`CLAUDE_TEAM_MODEL1_URL`	No	模型1 API 地址 (optional, defaults to MAIN URL)
`CLAUDE_TEAM_MODEL2_KEY`	No	模型2 API Key (optional, defaults to MAIN key)
`CLAUDE_TEAM_MODEL2_URL`	No	模型2 API 地址 (optional, defaults to MAIN URL)
`CLAUDE_TEAM_MODEL3_KEY`	No	模型3 API Key (optional, defaults to MAIN key)
`CLAUDE_TEAM_MODEL3_URL`	No	模型3 API 地址 (optional, defaults to MAIN URL)
`CLAUDE_TEAM_MODEL1_NAME`	No	模型1 ID (optional)
`CLAUDE_TEAM_MODEL2_NAME`	No	模型2 ID (optional)
`CLAUDE_TEAM_MODEL3_NAME`	No	模型3 ID (optional)
`CLAUDE_TEAM_MAIN_PROVIDER`	No	主模型提供商 (openai \| anthropic \| gemini)
`CLAUDE_TEAM_MODEL1_PROVIDER`	No	模型1提供商 (openai \| anthropic \| gemini)
`CLAUDE_TEAM_MODEL2_PROVIDER`	No	模型2提供商 (openai \| anthropic \| gemini)
`CLAUDE_TEAM_MODEL3_PROVIDER`	No	模型3提供商 (openai \| anthropic \| gemini)

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Features and capabilities supported by this server

Protocol revision2025-11-25

Capability	Details
`tools`	{}

Functions exposed to the LLM to take actions

Name	Description
team_workB	让 AI 开发团队协作完成任务。团队包含前端专家、后端专家、QA专家，会智能分配任务并互相协作。
ask_expertB	向特定专家咨询问题
code_reviewC	让专家审查代码
fix_bugD	让 QA 专家修复 Bug
history_listC	查看团队协作历史记录列表
history_getB	获取某次协作的详细记录
history_searchC	搜索协作历史记录
history_contextB	获取最近的协作上下文，可用于继续之前的工作
usage_statsA	查看各模型的使用统计（调用次数、成功率、平均耗时）
team_dashboardA	查看团队当前状态：可用专家、模型配置、最近活动
cost_estimateB	预估任务执行成本（Token 用量、预计耗时）
explain_planB	解释 Tech Lead 会如何分配任务（不实际执行）
read_project_filesC	读取项目文件内容，让专家了解代码上下文
generate_commit_messageC	根据代码变更生成 Git commit message
analyze_project_structureB	分析项目结构，识别技术栈和架构
list_workflowsA	列出所有可用的工作流模板
run_workflowB	使用指定工作流执行任务
suggest_workflowB	根据任务自动推荐合适的工作流

Interactive templates invoked by user choice

Name	Description
No prompts

Contextual data attached and managed by the client

Name	Description
No resources

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