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What is MCP?\n\nModel Context Protocol (MCP) is an open protocol that standardizes how AI applications provide context to Large Language Models (LLMs). It allows AI models to:\n\n- **Access Tools**: Execute functions and operations\n- **Read Resources**: Access data and content\n- **Use Prompts**: Leverage pre-defined prompt templates\n\nRead more: [MCP Specification](https://modelcontextprotocol.io/)\n\n## Installation\n\n```bash\npnpm add @objectstack/plugin-mcp-server\n```\n\n## Basic Usage\n\n```typescript\nimport { defineStack } from '@objectstack/spec';\nimport { PluginMCPServer } from '@objectstack/plugin-mcp-server';\n\nconst stack = defineStack({\n  plugins: [\n    PluginMCPServer.configure({\n      serverName: 'objectstack-server',\n      version: '1.0.0',\n      autoRegisterTools: true,\n    }),\n  ],\n});\n```\n\n## Configuration\n\n```typescript\ninterface MCPServerConfig {\n  /** Server name (shown to AI clients) */\n  serverName?: string;\n\n  /** Server version */\n  version?: string;\n\n  /** Auto-register tools from actions and flows */\n  autoRegisterTools?: boolean;\n\n  /** Auto-expose objects as resources */\n  autoExposeObjects?: boolean;\n\n  /** Enable streaming for large responses */\n  enableStreaming?: boolean;\n\n  /** Transport mechanism ('stdio' | 'http') */\n  transport?: 'stdio' | 'http';\n\n  /** HTTP port (if transport is 'http') */\n  port?: number;\n}\n```\n\n## MCP Tools\n\n### Auto-Generated Tools\n\nObjectStack automatically exposes these operations as MCP tools:\n\n```typescript\n// CRUD operations (auto-registered)\n'objectstack_find'         // Query records\n'objectstack_findOne'      // Get single record\n'objectstack_create'       // Create record\n'objectstack_update'       // Update record\n'objectstack_delete'       // Delete record\n\n// Metadata operations\n'objectstack_describeObject'   // Get object schema\n'objectstack_listObjects'      // List all objects\n'objectstack_listFields'       // List object fields\n```\n\n### Custom Tools\n\nRegister custom tools that AI models can call:\n\n```typescript\nimport { defineTool } from '@objectstack/spec';\n\nconst calculateRevenueTool = defineTool({\n  name: 'calculate_revenue',\n  description: 'Calculate total revenue for an account',\n  inputSchema: {\n    type: 'object',\n    properties: {\n      accountId: { type: 'string', description: 'Account ID' },\n      startDate: { type: 'string', description: 'Start date (ISO 8601)' },\n      endDate: { type: 'string', description: 'End date (ISO 8601)' },\n    },\n    required: ['accountId'],\n  },\n  async execute({ accountId, startDate, endDate }) {\n    const opportunities = await kernel.getDriver().find({\n      object: 'opportunity',\n      filters: [\n        { field: 'account_id', operator: 'eq', value: accountId },\n        { field: 'stage', operator: 'eq', value: 'closed_won' },\n        { field: 'close_date', operator: 'gte', value: startDate },\n        { field: 'close_date', operator: 'lte', value: endDate },\n      ],\n    });\n\n    const total = opportunities.reduce((sum, opp) => sum + opp.amount, 0);\n\n    return {\n      accountId,\n      totalRevenue: total,\n      opportunityCount: opportunities.length,\n    };\n  },\n});\n\n// Register with MCP server\nkernel.getService('mcp').registerTool(calculateRevenueTool);\n```\n\n## MCP Resources\n\n### Auto-Exposed Objects\n\nAll ObjectStack objects are automatically exposed as MCP resources:\n\n```\nobjectstack://objects/opportunity           # Opportunity object schema\nobjectstack://objects/opportunity/records   # All opportunity records\nobjectstack://objects/opportunity/123       # Specific opportunity record\n```\n\n### Custom Resources\n\nExpose custom resources to AI models:\n\n```typescript\nkernel.getService('mcp').registerResource({\n  uri: 'objectstack://reports/sales-pipeline',\n  name: 'Sales Pipeline Report',\n  description: 'Current sales pipeline with stages and amounts',\n  mimeType: 'application/json',\n  async read() {\n    const opportunities = await kernel.getDriver().find({\n      object: 'opportunity',\n      filters: [\n        { field: 'stage', operator: 'neq', value: 'closed_won' },\n        { field: 'stage', operator: 'neq', value: 'closed_lost' },\n      ],\n    });\n\n    const pipeline = opportunities.reduce((acc, opp) => {\n      acc[opp.stage] = (acc[opp.stage] || 0) + opp.amount;\n      return acc;\n    }, {});\n\n    return {\n      content: [\n        {\n          type: 'text',\n          text: JSON.stringify(pipeline, null, 2),\n        },\n      ],\n    };\n  },\n});\n```\n\n## MCP Prompts\n\nRegister prompt templates that AI models can use:\n\n```typescript\nkernel.getService('mcp').registerPrompt({\n  name: 'analyze_account',\n  description: 'Analyze an account and its opportunities',\n  arguments: [\n    {\n      name: 'accountId',\n      description: 'Account ID to analyze',\n      required: true,\n    },\n  ],\n  async render({ accountId }) {\n    const account = await kernel.getDriver().findOne({\n      object: 'account',\n      filters: [{ field: 'id', operator: 'eq', value: accountId }],\n    });\n\n    const opportunities = await kernel.getDriver().find({\n      object: 'opportunity',\n      filters: [{ field: 'account_id', operator: 'eq', value: accountId }],\n    });\n\n    return {\n      messages: [\n        {\n          role: 'user',\n          content: {\n            type: 'text',\n            text: `Analyze this account and provide insights:\n\nAccount: ${account.name}\nIndustry: ${account.industry}\nTotal Opportunities: ${opportunities.length}\nTotal Value: $${opportunities.reduce((sum, o) => sum + o.amount, 0)}\n\nOpportunities:\n${opportunities.map(o => `- ${o.name} (${o.stage}): $${o.amount}`).join('\\n')}\n\nPlease provide:\n1. Key insights about this account\n2. Risk assessment\n3. Recommendations for next steps`,\n          },\n        },\n      ],\n    };\n  },\n});\n```\n\n## Using with AI Clients\n\n### Claude Desktop\n\nAdd to `~/Library/Application Support/Claude/claude_desktop_config.json`:\n\n```json\n{\n  \"mcpServers\": {\n    \"objectstack\": {\n      \"command\": \"node\",\n      \"args\": [\"/path/to/your/objectstack/server.js\"],\n      \"env\": {\n        \"DATABASE_URL\": \"your-database-url\"\n      }\n    }\n  }\n}\n```\n\n### Cursor IDE\n\nAdd to `.cursor/mcp.json`:\n\n```json\n{\n  \"mcpServers\": {\n    \"objectstack\": {\n      \"command\": \"node\",\n      \"args\": [\"./server.js\"]\n    }\n  }\n}\n```\n\n### Cline VS Code Extension\n\nConfigure in Cline settings:\n\n```json\n{\n  \"cline.mcpServers\": {\n    \"objectstack\": {\n      \"command\": \"node\",\n      \"args\": [\"./server.js\"]\n    }\n  }\n}\n```\n\n## Server Implementation\n\n### Stdio Transport (Default)\n\n```typescript\n// server.ts\nimport { defineStack } from '@objectstack/spec';\nimport { PluginMCPServer } from '@objectstack/plugin-mcp-server';\nimport { DriverSql } from '@objectstack/driver-sql';\n\nconst stack = defineStack({\n  driver: DriverSql.configure({\n    client: 'better-sqlite3',\n    connection: { filename: process.env.DATABASE_URL ?? './data/app.db' },\n  }),\n  plugins: [\n    PluginMCPServer.configure({\n      serverName: 'my-crm',\n      transport: 'stdio', // Claude Desktop, Cursor, Cline\n    }),\n  ],\n});\n\nawait stack.boot();\n```\n\n### HTTP Transport\n\n```typescript\nconst stack = defineStack({\n  driver: DriverSql.configure({ /* ... */ }),\n  plugins: [\n    PluginMCPServer.configure({\n      serverName: 'my-crm',\n      transport: 'http',\n      port: 3100,\n    }),\n  ],\n});\n\nawait stack.boot();\n// MCP server running on http://localhost:3100\n```\n\n## Advanced Features\n\n### Streaming Resources\n\n```typescript\nkernel.getService('mcp').registerResource({\n  uri: 'objectstack://exports/opportunities-csv',\n  name: 'Opportunities Export (CSV)',\n  mimeType: 'text/csv',\n  async *stream() {\n    // Stream header\n    yield 'Name,Stage,Amount,Close Date\\n';\n\n    // Stream records in batches\n    let offset = 0;\n    const batchSize = 100;\n\n    while (true) {\n      const batch = await kernel.getDriver().find({\n        object: 'opportunity',\n        limit: batchSize,\n        offset,\n      });\n\n      if (batch.length === 0) break;\n\n      for (const opp of batch) {\n        yield `${opp.name},${opp.stage},${opp.amount},${opp.close_date}\\n`;\n      }\n\n      offset += batchSize;\n    }\n  },\n});\n```\n\n### Tool Permissions\n\n```typescript\nkernel.getService('mcp').registerTool({\n  name: 'delete_opportunity',\n  description: 'Delete an opportunity',\n  permissions: ['opportunity:delete'], // Require permission\n  inputSchema: {\n    type: 'object',\n    properties: {\n      id: { type: 'string' },\n    },\n    required: ['id'],\n  },\n  async execute({ id }, context) {\n    // context includes userId, permissions, etc.\n    if (!context.hasPermission('opportunity:delete')) {\n      throw new Error('Permission denied');\n    }\n\n    await kernel.getDriver().delete({\n      object: 'opportunity',\n      filters: [{ field: 'id', operator: 'eq', value: id }],\n    });\n\n    return { success: true, deleted: id };\n  },\n});\n```\n\n### Dynamic Tool Registration\n\n```typescript\n// Register tools from flow definitions\nconst flows = await kernel.getMetadata('flow');\n\nfor (const flow of flows) {\n  kernel.getService('mcp').registerTool({\n    name: `flow_${flow.name}`,\n    description: flow.description,\n    inputSchema: generateSchemaFromFlow(flow),\n    async execute(inputs) {\n      return await kernel.executeFlow(flow.name, inputs);\n    },\n  });\n}\n```\n\n## Server Capabilities\n\nThe MCP server exposes these capabilities:\n\n```json\n{\n  \"capabilities\": {\n    \"tools\": {\n      \"listChanged\": true\n    },\n    \"resources\": {\n      \"subscribe\": true,\n      \"listChanged\": true\n    },\n    \"prompts\": {\n      \"listChanged\": true\n    },\n    \"logging\": {},\n    \"experimental\": {\n      \"streaming\": true\n    }\n  }\n}\n```\n\n## Best Practices\n\n1. **Tool Design**: Keep tools focused and well-documented\n2. **Resource Naming**: Use clear, hierarchical URI schemes\n3. **Prompt Templates**: Make prompts flexible with arguments\n4. **Error Handling**: Always return helpful error messages\n5. **Permissions**: Check permissions before tool execution\n6. **Performance**: Use streaming for large datasets\n7. **Versioning**: Version your server and tools\n\n## Debugging\n\nEnable debug logging:\n\n```typescript\nPluginMCPServer.configure({\n  serverName: 'my-crm',\n  debug: true, // Log all MCP messages\n});\n```\n\nView MCP messages in client:\n- **Claude Desktop**: Check logs in `~/Library/Logs/Claude/mcp*.log`\n- **Cursor**: Check Output panel → MCP Server\n- **Cline**: Check extension logs\n\n## Example: Complete CRM Server\n\n```typescript\nimport { defineStack, defineTool } from '@objectstack/spec';\nimport { PluginMCPServer } from '@objectstack/plugin-mcp-server';\n\nconst stack = defineStack({\n  driver: /* ... */,\n  plugins: [\n    PluginMCPServer.configure({\n      serverName: 'crm-assistant',\n      autoRegisterTools: true,\n    }),\n  ],\n});\n\nawait stack.boot();\n\nconst mcp = stack.kernel.getService('mcp');\n\n// Register custom tools\nmcp.registerTool(defineTool({\n  name: 'forecast_revenue',\n  description: 'Forecast revenue based on pipeline',\n  async execute() {\n    // Implementation\n  },\n}));\n\n// Register custom resources\nmcp.registerResource({\n  uri: 'objectstack://dashboards/sales',\n  name: 'Sales Dashboard',\n  async read() {\n    // Implementation\n  },\n});\n\n// Register prompts\nmcp.registerPrompt({\n  name: 'weekly_report',\n  description: 'Generate weekly sales report',\n  async render() {\n    // Implementation\n  },\n});\n```\n\n## License\n\nApache-2.0. See [LICENSING.md](../../../LICENSING.md).\n\n## See Also\n\n- [Model Context Protocol Specification](https://modelcontextprotocol.io/)\n- [MCP TypeScript SDK](https://github.com/modelcontextprotocol/typescript-sdk)\n- [@objectstack/spec/ai](../../spec/src/ai/)\n- [Building MCP Servers Guide](/content/docs/guides/mcp/)\n","readmeFilename":"README.md"}