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Cornwell"},"license":"GPL-3.0-or-later","keywords":["AI","MCP","artificial","intelligence","model","context","protocol","integration"],"description":"A set of tools for making integration and aggregation of MCP servers extremely easy.","maintainers":[{"name":"macornwell","email":"michael.a.cornwell@gmail.com"}],"readme":"# The MCP Aggregation and Integration Library\n\n> Connect AI systems to MCPs, easily.\n\n![Unit Tests](https://github.com/leadership-4-tech/mcp-ai/actions/workflows/ut.yml/badge.svg?branch=main)\n[![Coverage Status](https://coveralls.io/repos/github/Leadership-4-Tech/mcp-ai/badge.svg?branch=try-again)](https://coveralls.io/github/Leadership-4-Tech/mcp-ai?branch=try-again)\n\nMCP servers are a pretty sweet idea, but having the ability to integrate them all together into a system, or package them up and make them available, should be easier. This is where this library comes in.\n\n## Installation\n\n```bash\nnpm install @l4t/mcp-ai\n```\n\n## Tools\n\nThe following are the tools available in this library\n\n- Aggregator: Aggregates multiple MCP servers under one configurable roof. Useful for translating supported protocols.\n- Integrator: Easier integration of MCP servers directly into LLM providers.\n- SimpleServer: Build a simple MCP configurable server, where you just provide tool descriptions and execution, it handles the rest. You can configure this to make the same server go from CLI to HTTP or SSE.\n\n## Creating an Integrator\n\nAn integrator is a tool that helps connect an LLM to an MCP server (like the aggregator). It can be used to format tools for the LLM provider, extract tool calls from the LLM response, and execute tool calls.\n\n```typescript\nimport { createIntegrator } from '@l4t/mcp-ai/integrator'\nimport { Provider } from '@l4t/mcp-ai'\n\n// Create an integrator configuration\nconst config = {\n  connection: {\n    type: 'http',\n    url: 'http://localhost:3000',\n    headers: {\n      'Content-Type': 'application/json',\n    },\n  },\n  provider: Provider.OpenAI,\n  model: 'gpt-4-turbo-preview',\n  maxParallelCalls: 1,\n}\n\n// Initialize the integrator\nconst integrator = createIntegrator(config)\n\n// Connect to the MCP server\nawait integrator.connect()\n\ntry {\n  // Get available tools\n  const tools = await integrator.getTools()\n\n  // Format tools for the LLM provider\n  const formattedTools = integrator.formatToolsForProvider(tools)\n\n  // Example of using the integrator with an LLM\n  const response = await llm.sendMessage('List available tools', formattedTools)\n\n  // Extract tool calls from the LLM response\n  const toolCalls = integrator.extractToolCalls(response)\n\n  // Execute the tool calls\n  const results = await integrator.executeToolCalls(toolCalls)\n\n  // Create a new request with the tool results\n  const newRequest = integrator.createToolResponseRequest(\n    originalRequest,\n    response,\n    results\n  )\n} finally {\n  // Always disconnect when done\n  await integrator.disconnect()\n}\n```\n\n## Creating an Aggregator\n\nAn aggregator is a MCP server that can aggregate multiple MCP servers into one. This can provide a single interface for AI to access multiple MCPs.\nThis can also be useful for adapting one type of MCP server to another. For example, if Cursor doesn't support http, you can support an http aggregator by putting a SSE aggregator in front.\n\n```typescript\nimport { create } from '@l4t/mcp-ai/aggregator'\n\n// Create an aggregator configuration\nconst config = {\n  server: {\n    connection: {\n      type: 'http',\n      url: 'http://localhost:3000',\n      port: 3000,\n    },\n    maxParallelCalls: 10,\n  },\n  mcps: [\n    {\n      id: 'filesystem',\n      connection: {\n        type: 'cli',\n        path: 'npx',\n        args: ['-y', '@modelcontextprotocol/server-memory'],\n      },\n    },\n    {\n      id: 'memory',\n      connection: {\n        type: 'cli',\n        path: 'npx',\n        args: ['-y', '@modelcontextprotocol/server-filesystem', '.'],\n      },\n    },\n  ],\n}\n\n// Create and start the aggregator server\nconst server = create(config)\n\n// Start the server\nawait server.start()\n\n// The server will now be available at http://localhost:3000\n// It will aggregate the tools from both the filesystem and memory MCPs\n\n// When done, stop the server\nawait server.stop()\n```\n\n## Creating a Simple Server\n\nA SimpleServer is a configurable MCP server that can be easily adapted to different protocols (HTTP, SSE, CLI) while maintaining the same tool functionality. This makes it perfect for building custom MCP servers that can be deployed in different environments.\n\n```typescript\nimport { create } from '@l4t/mcp-ai/simple-server'\n\n// Create a simple server configuration\nconst config = {\n  name: 'my-mcp-server',\n  version: '1.0.0',\n  tools: [\n    {\n      name: 'echo',\n      description: 'Echoes back the input',\n      inputSchema: {\n        type: 'object',\n        properties: {\n          message: { type: 'string' },\n        },\n        required: ['message'],\n      },\n      execute: async (input: { message: string }) => {\n        return { echo: input.message }\n      },\n    },\n  ],\n  server: {\n    connection: {\n      type: 'http',\n      port: 3000,\n    },\n  },\n}\n\n// Create and start the server\nconst server = create(config)\nawait server.start()\n\n// The server will now be available at http://localhost:3000\n// It will expose the echo tool and handle all MCP protocol details\n\n// When done, stop the server\nawait server.stop()\n```\n\n### Simple Server Configuration Examples\n\n#### HTTP Server\n\n```json\n{\n  \"name\": \"my-mcp-server\",\n  \"version\": \"1.0.0\",\n  \"tools\": [\n    {\n      \"name\": \"echo\",\n      \"description\": \"Echoes back the input\",\n      \"inputSchema\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"message\": { \"type\": \"string\" }\n        },\n        \"required\": [\"message\"]\n      }\n    }\n  ],\n  \"server\": {\n    \"connection\": {\n      \"type\": \"http\",\n      \"port\": 3000\n    }\n  }\n}\n```\n\n#### SSE Server\n\n```json\n{\n  \"name\": \"my-mcp-server\",\n  \"version\": \"1.0.0\",\n  \"tools\": [\n    {\n      \"name\": \"echo\",\n      \"description\": \"Echoes back the input\",\n      \"inputSchema\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"message\": { \"type\": \"string\" }\n        },\n        \"required\": [\"message\"]\n      }\n    }\n  ],\n  \"server\": {\n    \"connection\": {\n      \"type\": \"sse\",\n      \"port\": 3000\n    },\n    \"path\": \"/\",\n    \"messagesPath\": \"/messages\"\n  }\n}\n```\n\n#### CLI Server\n\n```json\n{\n  \"name\": \"my-mcp-server\",\n  \"version\": \"1.0.0\",\n  \"tools\": [\n    {\n      \"name\": \"echo\",\n      \"description\": \"Echoes back the input\",\n      \"inputSchema\": {\n        \"type\": \"object\",\n        \"properties\": {\n          \"message\": { \"type\": \"string\" }\n        },\n        \"required\": [\"message\"]\n      }\n    }\n  ],\n  \"server\": {\n    \"connection\": {\n      \"type\": \"cli\"\n    }\n  }\n}\n```\n\nThe SimpleServer makes it easy to:\n\n- Define your tools once and deploy them in different environments\n- Switch between protocols by just changing the configuration\n- Focus on your tool logic while the server handles MCP protocol details\n- Maintain consistent behavior across different transport mechanisms\n\n## Running Aggregator (server from CLI)\n\nIf you install this library globally it will add the `mcp-aggregator.mts` script to be used for starting up aggregators in any context.\n\n```bash\nnpm i -g @l4t/mcp-ai argparse\n```\n\nOnce you have it installed you can:\n\n```bash\nmcp-aggregator.js ./path-to-your-config.json\n```\n\nThis is very useful for running this as a server process, in something like a docker container.\n\n## Overview\n\nThis library is made up of two different domains.\n\n### Integration\n\nThis library has convenient and simple tooling for integrating MCP servers into LLM workflows with tools that do the formatting for you to and from the `@modelcontextprotocol/sdk` library.\n\n#### LLM Provider Support\n\nCurrently supports the following LLM Client's Format:\n\n- Openai\n- Anthropic\n- AWS Bedrock Claude\n\n#### A Note For Frontend Use\n\nMake sure you use an appropriate tree shaker to remove any aggregation code, because the aggregation code is completely\n\n### Aggregation\n\nThe aggregation tooling is used for taking many different MCP servers and putting them under one configuration roof. You can attach many different MCPs to a single system with just a configuration file.\n\nThis can be useful in scenarios where you want to package all of your MCPs into 1+ docker images, set them up with a single docker-compose.yml file, and then travel around with that docker compose file, empowering AI systems everywhere.\n\nYou can even save your configuration file with your system, making it clear what MCP's are required for your system to work. (Pretty cool huh?)\n\n#### MCP Connections Supported\n\n- CLI STDOUT IO\n- HTTP\n- SSE\n\n### Using Them Together\n\n`LLM Providers -> Integrator -> Aggregator -> MCP Servers`\n\n## Configuration Examples\n\n### Integrator Configurations\n\n#### CLI Integrator\n\n```json\n{\n  \"integrator\": {\n    \"connection\": {\n      \"type\": \"cli\",\n      \"path\": \"tsx\",\n      \"args\": [\"./bin/cliServer.mts\", \"./config.json\"]\n    },\n    \"provider\": \"aws-bedrock-claude\",\n    \"model\": \"anthropic.claude-3-5-sonnet-20241022-v2:0\",\n    \"modelId\": \"arn:aws:bedrock:us-east-1:461659650211:inference-profile/us.anthropic.claude-3-5-sonnet-20241022-v2:0\",\n    \"maxParallelCalls\": 1\n  }\n}\n```\n\n#### HTTP Integrator\n\n```json\n{\n  \"integrator\": {\n    \"connection\": {\n      \"type\": \"http\",\n      \"url\": \"http://localhost:3000\",\n      \"headers\": {\n        \"Content-Type\": \"application/json\"\n      }\n    },\n    \"provider\": \"openai\",\n    \"model\": \"gpt-4-turbo-preview\",\n    \"maxParallelCalls\": 1\n  }\n}\n```\n\n#### SSE Integrator\n\n```json\n{\n  \"integrator\": {\n    \"connection\": {\n      \"type\": \"sse\",\n      \"url\": \"http://localhost:3000\"\n    },\n    \"provider\": \"claude\",\n    \"model\": \"claude-3-opus-20240229\",\n    \"maxParallelCalls\": 1\n  }\n}\n```\n\n### Server Configurations\n\n#### CLI Server\n\n```json\n{\n  \"aggregator\": {\n    \"server\": {\n      \"connection\": {\n        \"type\": \"cli\"\n      },\n      \"maxParallelCalls\": 10\n    },\n    \"mcps\": [\n      {\n        \"id\": \"filesystem\",\n        \"connection\": {\n          \"type\": \"cli\",\n          \"path\": \"npx\",\n          \"args\": [\"-y\", \"@modelcontextprotocol/server-memory\"]\n        }\n      }\n    ]\n  }\n}\n```\n\n#### HTTP Server\n\n```json\n{\n  \"aggregator\": {\n    \"server\": {\n      \"connection\": {\n        \"type\": \"http\",\n        \"url\": \"http://localhost:3000\",\n        \"port\": 3000\n      },\n      \"path\": \"/\",\n      \"maxParallelCalls\": 10\n    },\n    \"mcps\": [\n      {\n        \"id\": \"filesystem\",\n        \"connection\": {\n          \"type\": \"cli\",\n          \"path\": \"npx\",\n          \"args\": [\"-y\", \"@modelcontextprotocol/server-memory\"]\n        }\n      }\n    ]\n  }\n}\n```\n\n#### SSE Server\n\n```json\n{\n  \"aggregator\": {\n    \"server\": {\n      \"connection\": {\n        \"type\": \"sse\",\n        \"url\": \"http://localhost:3000\",\n        \"port\": 3000\n      },\n      \"path\": \"/\",\n      \"messagesPath\": \"/messages\",\n      \"maxParallelCalls\": 10\n    },\n    \"mcps\": [\n      {\n        \"id\": \"filesystem\",\n        \"connection\": {\n          \"type\": \"cli\",\n          \"path\": \"npx\",\n          \"args\": [\"-y\", \"@modelcontextprotocol/server-memory\"]\n        }\n      }\n    ]\n  }\n}\n```\n\n### MCP Connection Variations\n\n#### CLI MCP Connection\n\n```json\n{\n  \"id\": \"memory\",\n  \"connection\": {\n    \"type\": \"cli\",\n    \"path\": \"npx\",\n    \"args\": [\"-y\", \"@modelcontextprotocol/server-memory\"],\n    \"env\": {\n      \"MEMORY_PATH\": \"./data\"\n    },\n    \"cwd\": \"./\"\n  }\n}\n```\n\n#### HTTP MCP Connection\n\n```json\n{\n  \"id\": \"filesystem\",\n  \"connection\": {\n    \"type\": \"http\",\n    \"url\": \"http://localhost:3001\",\n    \"headers\": {\n      \"Authorization\": \"Bearer your-token\"\n    },\n    \"timeout\": 5000,\n    \"retry\": {\n      \"attempts\": 3,\n      \"backoff\": 1000\n    }\n  }\n}\n```\n\n#### SSE MCP Connection\n\n```json\n{\n  \"id\": \"streaming\",\n  \"connection\": {\n    \"type\": \"sse\",\n    \"url\": \"http://localhost:3002\"\n  }\n}\n```\n\n#### WebSocket MCP Connection\n\n```json\n{\n  \"id\": \"realtime\",\n  \"connection\": {\n    \"type\": \"ws\",\n    \"url\": \"ws://localhost:3003\",\n    \"protocols\": [\"mcp-v1\"],\n    \"headers\": {\n      \"Authorization\": \"Bearer your-token\"\n    },\n    \"reconnect\": {\n      \"attempts\": 5,\n      \"backoff\": 1000\n    }\n  }\n}\n```\n\n### Full Configuration Example\n\nA complete configuration combining both integrator and aggregator:\n\n```json\n{\n  \"integrator\": {\n    \"connection\": {\n      \"type\": \"cli\",\n      \"path\": \"tsx\",\n      \"args\": [\"./bin/cliServer.mts\", \"./config.json\"]\n    },\n    \"provider\": \"aws-bedrock-claude\",\n    \"model\": \"anthropic.claude-3-5-sonnet-20241022-v2:0\",\n    \"modelId\": \"arn:aws:bedrock:us-east-1:461659650211:inference-profile/us.anthropic.claude-3-5-sonnet-20241022-v2:0\",\n    \"maxParallelCalls\": 1\n  },\n  \"aggregator\": {\n    \"server\": {\n      \"connection\": {\n        \"type\": \"cli\"\n      },\n      \"maxParallelCalls\": 10\n    },\n    \"mcps\": [\n      {\n        \"id\": \"filesystem\",\n        \"connection\": {\n          \"type\": \"cli\",\n          \"path\": \"npx\",\n          \"args\": [\"-y\", \"@modelcontextprotocol/server-memory\"]\n        }\n      }\n    ]\n  }\n}\n```\n\n## Testing\n\nThere are a few examples included in the\n\n### Environment Variables\n\nDepending on the provider, you may need to set these environment variables for tests:\n\n- OpenAI: `OPENAI_API_KEY`\n- Claude: `ANTHROPIC_API_KEY`\n- AWS Bedrock: AWS credentials configured in your environment\n\n## Notes\n\n- The `modelId` field is required for AWS Bedrock and should be the ARN of your model\n- For HTTP and SSE servers, the `port` field is optional and defaults to 3000\n- The `path` field in server configurations is optional and defaults to '/'\n- For SSE servers, the `messagesPath` field is optional and defaults to '/messages'\n- `maxParallelCalls` is optional and defaults to 1 for integrators and 10 for servers\n\n## Contributing\n\n1. Fork the repository\n2. Create your feature branch\n3. Commit your changes\n4. Push to the branch\n5. Create a new Pull Request\n\n## License\n\nGPL v3\n","readmeFilename":"README.md"}