mcp-create-declarative-agent

tarafından github

Microsoft 365 Copilot için bir MCP sunucusuyla entegre bildirimsel bir ajan iskeleti oluşturur. MCP sunucularından otomatik olarak içe aktarılan araç tanımlarıyla manifest.json, declarativeAgent.json ve ai-plugin.json dosyalarını içeren eksiksiz proje yapısı üretir. Ortam değişkeni yapılandırması ve güvenli kimlik bilgisi depolama ile OAuth 2.0 ve SSO kimlik doğrulamasını destekler. Copilot tüketimi için API yanıt verilerini çıkarmak ve biçimlendirmek üzere yanıt anlam haritalaması sağlar. MCP sunucu entegrasyon iş akışını içerir...

npx skills add https://github.com/github/awesome-copilot --skill mcp-create-declarative-agent
---
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:
```json
{
  "$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:
```json
{
  "$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:
```json
{
  "schema_version": "v2.1",
  "name_for_human": "Service Name",
  "description_for_human": "Description for users",
  "description_for_model": "Description for AI model",
  "contact_email": "[email protected]",
  "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:
```json
{
  "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)**
```json
"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)**
```json
"auth": {
  "type": "SSO"
}
```

## Response Semantics

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

```json
"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:

```env
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

github tarafından daha fazla skill

console-rendering
github
Go'da struct etiketi tabanlı konsol renderlama sistemini kullanma talimatları
official
acquire-codebase-knowledge
github
Bu beceriyi, kullanıcı açıkça mevcut bir kod tabanını haritalamayı, belgelemeyi veya bu kod tabanına dahil olmayı istediğinde kullanın. "Bu kod tabanını haritala", "belgele…" gibi ifadeler için tetikleyin.
official
acreadiness-assess
github
Run the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc…
official
acreadiness-generate-instructions
github
AgentRC talimatları komutu aracılığıyla özelleştirilmiş AI ajan talimat dosyaları oluşturur. .github/copilot-instructions.md dosyasını üretir (varsayılan, VS'de Copilot için önerilir…
official
acreadiness-policy
github
Kullanıcının bir AgentRC politikası seçmesine, yazmasına veya uygulamasına yardımcı olun. Politikalar, ilgisiz kontrolleri devre dışı bırakarak, etki/seviyeyi geçersiz kılarak, ayarlayarak…
official
add-educational-comments
github
We need to translate the given English text into Turkish, preserving the name "add-educational-comments" if it appears. The text is a description of an agent skill. We must not add any extra commentary, labels, or formatting. The translation should be accurate and natural in Turkish. The text: "Add educational comments to code files to transform them into effective learning resources. Adapts explanation depth and tone to three configurable knowledge levels: beginner, intermediate, and advanced Automatically requests a file if none is provided, with numbered list matching for quick selection Expands files by up to 125% using educational comments only (hard limit: 400 new lines; 300 for files over 1,000 lines) Preserves file encoding, indentation style, syntax correctness, and..." It seems cut off at the end. The original might have more, but we only have this. We'll translate what's given. Note: The name "add-educational-comments" does not appear in the text, so we don't include it. Translation: "Kod dosyalarına
official
adobe-illustrator-scripting
github
ExtendScript (JavaScript/JSX) kullanarak Adobe Illustrator otomasyon betiklerini yazın, hata ayıklayın ve optimize edin. Oluştururken veya değiştirirken kullanın…
official
agent-governance
github
Yapay zeka aracı erişimi ve davranışını kontrol etmek için bildirimsel politikalar, niyet sınıflandırması ve denetim izleri. Birleştirilebilir yönetişim politikaları, izin verilen/engellenen araçları, içerik filtrelerini, hız sınırlarını ve onay gereksinimlerini tanımlar — kod değil yapılandırma olarak saklanır. Anlamsal niyet sınıflandırması, araç yürütülmeden önce desen tabanlı sinyaller kullanarak tehlikeli istemleri (veri sızdırma, ayrıcalık yükseltme, istem enjeksiyonu) tespit eder. Araç düzeyinde yönetişim dekoratörü, politikaları işlevde u
official