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bridge MCP clients with AI providers","maintainers":[{"name":"theplenkov-npm","email":"petr.plenkov@gmail.com"}],"readme":"# @anygpt/mcp\n\n> **⚠️ WORK IN PROGRESS**: This package is under active development. APIs and MCP tools may change significantly. Use at your own risk in production environments.\n\nA Model Context Protocol (MCP) server that bridges MCP clients with multiple AI providers through the AnyGPT router system.\n\n## Features\n\n- **Multi-Provider Support**: Currently supports OpenAI, with plans for Anthropic and local models\n- **MCP Protocol Compliance**: Full implementation of MCP specification using official TypeScript SDK\n- **Secure Gateway Architecture**: Credentials managed by gateway service, not exposed to MCP clients\n- **Tool-Based Interface**: Exposes AI functionality through MCP tools\n- **Self-Documenting Resources**: AI agents can read documentation about providers and workflows\n- **Pre-Built Prompts**: Ready-to-use templates for common tasks like discovery and comparison\n\n## Available Tools\n\n### `anygpt_list_providers`\n\nDiscover all configured AI providers and their types. **Use this first** to understand what providers are available.\n\n**Parameters:** None\n\n**Returns:**\n\n- `providers`: Array of provider information\n  - `id`: Provider identifier (used in other tools)\n  - `type`: Provider type (e.g., \"openai\", \"anthropic\")\n  - `isDefault`: Whether this is the default provider\n- `default_provider`: The default provider ID (if configured)\n\n**Example response:**\n\n```json\n{\n  \"providers\": [\n    { \"id\": \"openai\", \"type\": \"openai\", \"isDefault\": true },\n    { \"id\": \"anthropic\", \"type\": \"anthropic\", \"isDefault\": false }\n  ],\n  \"default_provider\": \"openai\"\n}\n```\n\n### `anygpt_list_models`\n\nList available models dynamically from AI providers. Requires valid API credentials.\n\n**Parameters:**\n\n- `provider` (optional): AI provider to list models from (uses default if not specified)\n\n**Note:** This tool fetches models in real-time from the provider's API, ensuring you always get the most up-to-date list of available models.\n\n### `anygpt_chat_completion`\n\nSend chat completion requests to AI providers via the gateway.\n\n**Parameters:**\n\n- `messages` (required): Array of chat messages with `role` and `content`\n- `model` (optional): Model to use (uses default if not specified)\n- `provider` (optional): AI provider (uses default if not specified)\n- `temperature` (optional): Sampling temperature (0-2, default: 1)\n- `max_tokens` (optional): Maximum tokens to generate (default: 4096)\n\n**Understanding Response Truncation:**\n\nIf the response has `\"finish_reason\": \"length\"`, it means the output was **truncated** because it reached the `max_tokens` limit. To get a complete response:\n\n1. **Increase `max_tokens`** in your request\n2. **Use these guidelines:**\n   - Short answers: 100-500 tokens\n   - Medium responses: 500-2000 tokens\n   - Long/comprehensive outputs: 2000-4096+ tokens\n3. **Trade-offs:** Higher `max_tokens` values increase latency and cost but prevent truncation\n\n**Example of handling truncation:**\n\n```typescript\n// First attempt - might be truncated\nconst response1 = await anygpt_chat_completion({\n  messages: [{ role: 'user', content: 'Explain quantum computing in detail' }],\n  max_tokens: 100, // Too low!\n});\n\nif (response1.choices[0].finish_reason === 'length') {\n  // Response was truncated, retry with higher limit\n  const response2 = await anygpt_chat_completion({\n    messages: [\n      { role: 'user', content: 'Explain quantum computing in detail' },\n    ],\n    max_tokens: 2000, // Much better!\n  });\n}\n```\n\n## Discovery Workflow\n\nThe MCP server is **self-documenting**. Follow this workflow to discover and use available providers and models:\n\n1. **Discover Providers**: Call `anygpt_list_providers` to see what AI providers are configured\n2. **List Models**: Call `anygpt_list_models` with a specific provider to see available models\n3. **Chat**: Call `anygpt_chat_completion` with your chosen provider and model\n\n**Example:**\n\n```typescript\n// Step 1: What providers are available?\nanygpt_list_providers();\n// Returns: { providers: [{ id: \"openai\", type: \"openai\", isDefault: true }], ... }\n\n// Step 2: What models does OpenAI offer?\nanygpt_list_models({ provider: 'openai' });\n// Returns: { provider: \"openai\", models: [...] }\n\n// Step 3: Use a specific model\nanygpt_chat_completion({\n  provider: 'openai',\n  model: 'gpt-4',\n  messages: [{ role: 'user', content: 'Hello!' }],\n});\n```\n\n## Resources (AI-Readable Documentation)\n\nThe server exposes documentation using **resource templates** that AI agents can read:\n\n### Templates\n\n- **`anygpt://docs/{section}`** - Access documentation sections\n  - Available sections: `overview`, `workflow`, `providers`\n- **`anygpt://examples/{example}`** - Access usage examples\n  - Available examples: `basic-chat`\n\n### Example URIs\n\n- `anygpt://docs/overview` - Introduction to AnyGPT MCP server\n- `anygpt://docs/workflow` - Step-by-step discovery workflow guide\n- `anygpt://docs/providers` - Currently configured providers (dynamic)\n- `anygpt://examples/basic-chat` - Complete usage example\n\n**Benefits**: Resource templates reduce token overhead when listing resources and allow for dynamic, scalable documentation.\n\n## Sampling (AI Assistant Integration)\n\nThe MCP server supports **sampling**, which allows AI assistants (VS Code Copilot, Windsurf, Claude Desktop) to use your configured providers for completions.\n\n### How It Works\n\n1. **Enable in VS Code**: Click \"Configure Model Access\" in the MCP server menu\n2. **Select Models**: Choose which models to make available (e.g., `opus`, `gpt-4`, `sonnet`)\n3. **AI Uses Your Server**: The AI assistant calls `anygpt_chat_completion` through your MCP server\n4. **Smart Resolution**: Your config's aliases and tags work automatically\n\n### Available Models\n\nYou can use any of:\n\n- **Aliases** from your config (e.g., `opus`, `fast`, `cheap`)\n- **Tags** from provider models (e.g., `flagship`, `best`)\n- **Direct model names** (e.g., `gpt-4`, `claude-opus-4-20250514`)\n\n### Benefits\n\n✅ **Unified Configuration** - Same config for CLI, MCP, and IDE  \n✅ **Smart Resolution** - Use shortcuts instead of full model names  \n✅ **Cost Control** - Route to appropriate models based on task  \n✅ **Provider Flexibility** - Switch providers without reconfiguring IDE\n\n## Prompts (Pre-Built Workflows)\n\nThe server provides ready-to-use prompt templates for common tasks:\n\n### `discover-and-chat`\n\nComplete workflow that discovers providers, lists models, and sends a chat message.\n\n**Arguments:**\n\n- `question` (required): The question to ask the AI\n\n### `compare-providers`\n\nCompare how different AI providers respond to the same question.\n\n**Arguments:**\n\n- `question` (required): The question to ask all providers\n\n### `list-capabilities`\n\nShow all available providers and their models in an organized format.\n\n**No arguments required**\n\nThese prompts guide AI agents through complex multi-step workflows automatically.\n\n## Installation\n\n```bash\n# Install dependencies\nnpm install\n\n# Build the project\nnpx nx build mcp\n```\n\n## Configuration\n\nThe server is configured via environment variables:\n\n| Variable         | Description                     | Default         |\n| ---------------- | ------------------------------- | --------------- |\n| `OPENAI_API_KEY` | OpenAI API key                  | -               |\n| `DEFAULT_MODEL`  | Default model to use            | `gpt-3.5-turbo` |\n| `TIMEOUT`        | Request timeout in milliseconds | `30000`         |\n| `MAX_RETRIES`    | Maximum retry attempts          | `3`             |\n\n## Usage\n\n### Running the Server\n\n```bash\n# Run directly\nnode ./dist/index.js\n\n# Or if installed globally\nanygpt-mcp\n```\n\n### Testing with MCP Inspector\n\n```bash\n# Install MCP Inspector\nnpm install -g @modelcontextprotocol/inspector\n\n# Test the server\nnpx @modelcontextprotocol/inspector node ./dist/index.js\n```\n\n### Claude Desktop Integration\n\nAdd to your Claude Desktop configuration:\n\n```json\n{\n  \"mcpServers\": {\n    \"anygpt\": {\n      \"command\": \"node\",\n      \"args\": [\"/absolute/path/to/dist/index.js\"],\n      \"env\": {\n        \"OPENAI_API_KEY\": \"your-openai-api-key\"\n      }\n    }\n  }\n}\n```\n\n### Docker Desktop MCP Toolkit Integration\n\n```json\n{\n  \"name\": \"AnyGPT MCP\",\n  \"command\": \"node\",\n  \"args\": [\"/path/to/dist/index.js\"],\n  \"env\": {\n    \"OPENAI_API_KEY\": \"your-openai-api-key\",\n    \"DEFAULT_MODEL\": \"gpt-4\"\n  }\n}\n```\n\n## Architecture\n\n```\nMCP Client → @anygpt/mcp → @anygpt/router → AI Provider APIs\n```\n\nThe server acts as a protocol translator:\n\n1. Receives MCP tool calls from clients\n2. Translates them to router API calls\n3. Routes requests to appropriate AI providers via connectors\n4. Returns responses in MCP format\n\n## Development\n\n```bash\n# Build the project\nnpx nx build mcp\n\n# Run tests\nnpx nx test mcp\n\n# Lint code\nnpx nx lint mcp\n```\n\n## Security\n\n- API keys are managed by the gateway service\n- No direct API key exposure to MCP clients\n- Input validation on all requests\n- Secure transport over stdio\n\n## License\n\nMIT\n","readmeFilename":"README.md"}