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Agent Skill · 研究检索

mcp-create-declarative-agent(MCP create declarative Agent 搜索)

mcp-create-declarative-agent 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

mcp-create-declarative-agent 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 681。

总安装 199,592周安装 8,327GitHub Stars 31,730下载 66,584核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:mcp-create-declarative-agent(MCP create declarative Agent 搜索)
来源仓库:https://github.com/github/awesome-copilot
仓库路径:skills/mcp-create-declarative-agent
安装命令:
npx skills add https://github.com/github/awesome-copilot --skill mcp-create-declarative-agent
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/github/awesome-copilot --skill mcp-create-declarative-agent

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

Scaffold 是与 MCP 服务器集成的 Microsoft 365 Copilot 声明式代理。

生成完整的项目结构,包括manifest.json、declarativeAgent.json和ai-plugin.json,并具有从MCP服务器自动导入的工具定义 通过环境变量配置和安全凭证存储支持 OAuth 2.0 和 SSO 身份验证 提供响应语义映射以提取和格式化 API 响应数据以供 Copilot 使用 包括 MCP 服务器集成工作流程,涵盖工具选择、端点验证和通过 Teams 侧载进行的本地测试

来源仓库内路径:skills/mcp-create-declarative-agent

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/github/awesome-copilot --skill mcp-create-decl…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

mcp-create-declarative-agent 怎么安装?

在终端执行 npx skills add https://github.com/github/awesome-copilot --skill mcp-create-declarative-ag…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

mcp-create-declarative-agent 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

mcp-create-declarative-agent 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

mcp-create-declarative-agent 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

mcp-create-declarative-agent 用的人多吗?

公开数据是累计安装 199,592 次,近一周安装 8,327 次,下载 66,584 次,GitHub 31,730 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

mcp-create-declarative-agent 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 来源仓库有 31,730 星,维护活跃度相对可查。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
mcp-create-declarative-agent

MCP create declarative Agent 搜索 · 本页

199,59231,730接触敏感数据未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

---
mode: 'agent'
tools: ['changes', 'search/codebase', 'edit/editFiles', 'problems']
description: 'Create a declarative agent for Microsoft 365 Copilot by integrating an MCP server with authentication, tool selection, and configuration'
model: 'gpt-4.1'
tags: [mcp, m365-copilot, declarative-agent, model-context-protocol, api-plugin]
---

# Create MCP-based Declarative Agent for Microsoft 365 Copilot

Create a complete declarative agent for Microsoft 365 Copilot that integrates with a Model Context Protocol (MCP) server to access external systems and data.

## Requirements

Generate the following project structure using Microsoft 365 Agents Toolkit:

### Project Setup
1. **Scaffold declarative agent** via Agents Toolkit
2. **Add MCP action** pointing to MCP server
3. **Select tools** to import from MCP server
4. **Configure authentication** (OAuth 2.0 or SSO)
5. **Review generated files** (manifest.json, ai-plugin.json, declarativeAgent.json)

### Key Files Generated

**appPackage/manifest.json** - Teams app manifest with plugin reference:

{ "$schema": "https://developer.microsoft.com/json-schemas/teams/vDevPreview/MicrosoftTeams.schema.json", "manifestVersion": "devPreview", "version": "1.0.0", "id": "...", "developer": { "name": "...", "websiteUrl": "...", "privacyUrl": "...", "termsOfUseUrl": "..." }, "name": { "short": "Agent Name", "full": "Full Agent Name" }, "description": { "short": "Short description", "full": "Full description" }, "copilotAgents": { "declarativeAgents": [ { "id": "declarativeAgent", "file": "declarativeAgent.json" } ] } }


**appPackage/declarativeAgent.json** - Agent definition:

{ "$schema": "https://aka.ms/json-schemas/copilot/declarative-agent/v1.0/schema.json", "version": "v1.0", "name": "Agent Name", "description": "Agent description", "instructions": "You are an assistant that helps with [specific domain]. Use the available tools to [capabilities].", "capabilities": [ { "name": "WebSearch", "websites": [ { "url": "https://learn.microsoft.com" } ] }, { "name": "MCP", "file": "ai-plugin.json" } ] }


**appPackage/ai-plugin.json** - MCP plugin manifest:

{ "schema_version": "v2.1", "name_for_human": "Service Name", "description_for_human": "Description for users", "description_for_model": "Description for AI model", "contact_email": "support@company.com", "namespace": "serviceName", "capabilities": { "conversation_starters": [ { "text": "Example query 1" } ] }, "functions": [ { "name": "functionName", "description": "Function description", "capabilities": { "response_semantics": { "data_path": "$", "properties": { "title": "$.title", "subtitle": "$.description" } } } } ], "runtimes": [ { "type": "MCP", "spec": { "url": "https://api.service.com/mcp/" }, "run_for_functions": ["functionName"], "auth": { "type": "OAuthPluginVault", "reference_id": "${{OAUTH_REFERENCE_ID}}" } } ] }


**/.vscode/mcp.json** - MCP server configuration:

{ "serverUrl": "https://api.service.com/mcp/", "pluginFilePath": "appPackage/ai-plugin.json" }


## MCP Server Integration

### Supported MCP Endpoints
The MCP server must provide:
- **Server metadata** endpoint
- **Tools listing** endpoint (exposes available functions)
- **Tool execution** endpoint (handles function calls)

### Tool Selection
When importing from MCP:
1. Fetch available tools from server
2. Select specific tools to include (for security/simplicity)
3. Tool definitions are auto-generated in ai-plugin.json

### Authentication Types

**OAuth 2.0 (Static Registration)**

"auth": { "type": "OAuthPluginVault", "reference_id": "${{OAUTH_REFERENCE_ID}}", "authorization_url": "https://auth.service.com/authorize", "client_id": "${{CLIENT_ID}}", "client_secret": "${{CLIENT_SECRET}}", "scope": "read write" }


**Single Sign-On (SSO)**

"auth": { "type": "SSO" }


## Response Semantics

### Define Data Mapping
Use `response_semantics` to extract relevant fields from API responses:

"capabilities": { "response_semantics": { "data_path": "$.results", "properties": { "title": "$.name", "subtitle": "$.description", "url": "$.link" } } }


### Add Adaptive Cards (Optional)
See the `mcp-create-adaptive-cards` prompt for adding visual card templates.

## Environment Configuration

Create `.env.local` or `.env.dev` for credentials:

OAUTH_REFERENCE_ID=your-oauth-reference-id CLIENT_ID=your-client-id CLIENT_SECRET=your-client-secret


## Testing & Deployment

### Local Testing
1. **Provision** agent in Agents Toolkit
2. **Start debugging** to sideload in Teams
3. Test in Microsoft 365 Copilot at https://m365.cloud.microsoft/chat
4. Authenticate when prompted
5. Query the agent using natural language

### Validation
- Verify tool imports in ai-plugin.json
- Check authentication configuration
- Test each exposed function
- Validate response data mapping

## Best Practices

### Tool Design
- **Focused functions**: Each tool should do one thing well
- **Clear descriptions**: Help the model understand when to use each tool
- **Minimal scoping**: Only import tools the agent needs
- **Descriptive names**: Use action-oriented function names

### Security
- **Use OAuth 2.0** for production scenarios
- **Store secrets** in environment variables
- **Validate inputs** on the MCP server side
- **Limit scopes** to minimum required permissions
- **Use reference IDs** for OAuth registration

### Instructions
- **Be specific** about the agent's purpose and capabilities
- **Define behavior** for both successful and error scenarios
- **Reference tools** explicitly in instructions when applicable
- **Set expectations** for users about what the agent can/cannot do

### Performance
- **Cache responses** when appropriate on MCP server
- **Batch operations** where possible
- **Set timeouts** for long-running operations
- **Paginate results** for large datasets

## Common MCP Server Examples

### GitHub MCP Server

URL: https://api.githubcopilot.com/mcp/ Tools: search_repositories, search_users, get_repository Auth: OAuth 2.0


### Jira MCP Server

URL: https://your-domain.atlassian.net/mcp/ Tools: search_issues, create_issue, update_issue Auth: OAuth 2.0


### Custom Service

URL: https://api.your-service.com/mcp/ Tools: Custom tools exposed by your service Auth: OAuth 2.0 or SSO


## Workflow

Ask the user:
1. What MCP server are you integrating with (URL)?
2. What tools should be exposed to Copilot?
3. What authentication method does the server support?
4. What should the agent's primary purpose be?
5. Do you need response semantics or Adaptive Cards?

Then generate:
- Complete appPackage/ structure (manifest.json, declarativeAgent.json, ai-plugin.json)
- mcp.json configuration
- .env.local template
- Provisioning and testing instructions

## Troubleshooting

### MCP Server Not Responding
- Verify server URL is correct
- Check network connectivity
- Validate MCP server implements required endpoints

### Authentication Fails
- Verify OAuth credentials are correct
- Check reference ID matches registration
- Confirm scopes are requested properly
- Test OAuth flow independently

### Tools Not Appearing
- Ensure mcp.json points to correct server
- Verify tools were selected during import
- Check ai-plugin.json has correct function definitions
- Re-fetch actions from MCP if server changed

### Agent Not Understanding Queries
- Review instructions in declarativeAgent.json
- Check function descriptions are clear
- Verify response_semantics extract correct data
- Test with more specific queries

平台分布

Codex

38.2%
按下载量换算25,435

Claude

28.21%
按下载量换算18,783

Cursor

19.26%
按下载量换算12,824

Gemini CLI

9.26%
按下载量换算6,166

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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