Dedalus TypeScript MCP Server

It is generated with Stainless.

Installation

Direct invocation

You can run the MCP Server directly via npx:

export DEDALUS_API_KEY="My API Key"
export DEDALUS_X_API_KEY="My X API Key"
export DEDALUS_ORG_ID="My Organization"
export DEDALUS_PROVIDER="My Provider"
export DEDALUS_PROVIDER_KEY="My Provider Key"
export DEDALUS_PROVIDER_MODEL="My Provider Model"
export DEDALUS_ENVIRONMENT="production"
npx -y dedalus-labs-mcp@latest

Via MCP Client

There is a partial list of existing clients at modelcontextprotocol.io. If you already have a client, consult their documentation to install the MCP server.

For clients with a configuration JSON, it might look something like this:

{
  "mcpServers": {
    "dedalus_labs_api": {
      "command": "npx",
      "args": ["-y", "dedalus-labs-mcp", "--client=claude", "--tools=all"],
      "env": {
        "DEDALUS_API_KEY": "My API Key",
        "DEDALUS_X_API_KEY": "My X API Key",
        "DEDALUS_ORG_ID": "My Organization",
        "DEDALUS_PROVIDER": "My Provider",
        "DEDALUS_PROVIDER_KEY": "My Provider Key",
        "DEDALUS_PROVIDER_MODEL": "My Provider Model",
        "DEDALUS_ENVIRONMENT": "production"
      }
    }
  }
}

Cursor

If you use Cursor, you can install the MCP server by using the button below. You will need to set your environment variables in Cursor's mcp.json, which can be found in Cursor Settings > Tools & MCP > New MCP Server.

VS Code

If you use MCP, you can install the MCP server by clicking the link below. You will need to set your environment variables in VS Code's mcp.json, which can be found via Command Palette > MCP: Open User Configuration.

Open VS Code

Claude Code

If you use Claude Code, you can install the MCP server by running the command below in your terminal. You will need to set your environment variables in Claude Code's .claude.json, which can be found in your home directory.

claude mcp add --transport stdio dedalus_labs_api --env DEDALUS_API_KEY="Your DEDALUS_API_KEY here." DEDALUS_X_API_KEY="Your DEDALUS_X_API_KEY here." DEDALUS_ORG_ID="Your DEDALUS_ORG_ID here." DEDALUS_PROVIDER="Your DEDALUS_PROVIDER here." DEDALUS_PROVIDER_KEY="Your DEDALUS_PROVIDER_KEY here." DEDALUS_PROVIDER_MODEL="Your DEDALUS_PROVIDER_MODEL here." -- npx -y dedalus-labs-mcp

Exposing endpoints to your MCP Client

There are three ways to expose endpoints as tools in the MCP server:

1. Exposing one tool per endpoint, and filtering as necessary
2. Exposing a set of tools to dynamically discover and invoke endpoints from the API
3. Exposing a docs search tool and a code execution tool, allowing the client to write code to be executed against the TypeScript client

Filtering endpoints and tools

You can run the package on the command line to discover and filter the set of tools that are exposed by the MCP Server. This can be helpful for large APIs where including all endpoints at once is too much for your AI's context window.

You can filter by multiple aspects:

• --tool includes a specific tool by name
• --resource includes all tools under a specific resource, and can have wildcards, e.g. my.resource*
• --operation includes just read (get/list) or just write operations

Dynamic tools

If you specify --tools=dynamic to the MCP server, instead of exposing one tool per endpoint in the API, it will expose the following tools:

1. list_api_endpoints - Discovers available endpoints, with optional filtering by search query
2. get_api_endpoint_schema - Gets detailed schema information for a specific endpoint
3. invoke_api_endpoint - Executes any endpoint with the appropriate parameters

This allows you to have the full set of API endpoints available to your MCP Client, while not requiring that all of their schemas be loaded into context at once. Instead, the LLM will automatically use these tools together to search for, look up, and invoke endpoints dynamically. However, due to the indirect nature of the schemas, it can struggle to provide the correct properties a bit more than when tools are imported explicitly. Therefore, you can opt-in to explicit tools, the dynamic tools, or both.

See more information with --help.

All of these command-line options can be repeated, combined together, and have corresponding exclusion versions (e.g. --no-tool).

Use --list to see the list of available tools, or see below.

Code execution

If you specify --tools=code to the MCP server, it will expose just two tools:

• search_docs - Searches the API documentation and returns a list of markdown results
• execute - Runs code against the TypeScript client

This allows the LLM to implement more complex logic by chaining together many API calls without loading intermediary results into its context window.

The code execution itself happens in a Deno sandbox that has network access only to the base URL for the API.

Specifying the MCP Client

Different clients have varying abilities to handle arbitrary tools and schemas.

You can specify the client you are using with the --client argument, and the MCP server will automatically serve tools and schemas that are more compatible with that client.

• --client=<type>: Set all capabilities based on a known MCP client

  • Valid values: openai-agents, claude, claude-code, cursor
  • Example: --client=cursor

Additionally, if you have a client not on the above list, or the client has gotten better over time, you can manually enable or disable certain capabilities:

• --capability=<name>: Specify individual client capabilities
  • Available capabilities:
    • top-level-unions: Enable support for top-level unions in tool schemas
    • valid-json: Enable JSON string parsing for arguments
    • refs: Enable support for $ref pointers in schemas
    • unions: Enable support for union types (anyOf) in schemas
    • formats: Enable support for format validations in schemas (e.g. date-time, email)
    • tool-name-length=N: Set maximum tool name length to N characters
  • Example: --capability=top-level-unions --capability=tool-name-length=40
  • Example: --capability=top-level-unions,tool-name-length=40

Examples

1. Filter for read operations on cards:

--resource=cards --operation=read

2. Exclude specific tools while including others:

--resource=cards --no-tool=create_cards

3. Configure for Cursor client with custom max tool name length:

--client=cursor --capability=tool-name-length=40

4. Complex filtering with multiple criteria:

--resource=cards,accounts --operation=read --tag=kyc --no-tool=create_cards

Running remotely

Launching the client with --transport=http launches the server as a remote server using Streamable HTTP transport. The --port setting can choose the port it will run on, and the --socket setting allows it to run on a Unix socket.

Authorization can be provided via the Authorization header using the Bearer scheme.

Additionally, authorization can be provided via the following headers: | Header | Equivalent client option | Security scheme | | ------------------- | ------------------------ | --------------- | | x-dedalus-api-key | apiKey | Bearer | | x-api-key | xAPIKey | ApiKeyAuth |

A configuration JSON for this server might look like this, assuming the server is hosted at http://localhost:3000:

{
  "mcpServers": {
    "dedalus_labs_api": {
      "url": "http://localhost:3000",
      "headers": {
        "Authorization": "Bearer <auth value>"
      }
    }
  }
}

The command-line arguments for filtering tools and specifying clients can also be used as query parameters in the URL. For example, to exclude specific tools while including others, use the URL:

http://localhost:3000?resource=cards&resource=accounts&no_tool=create_cards

Or, to configure for the Cursor client, with a custom max tool name length, use the URL:

http://localhost:3000?client=cursor&capability=tool-name-length%3D40

Importing the tools and server individually

// Import the server, generated endpoints, or the init function
import { server, endpoints, init } from "dedalus-labs-mcp/server";

// import a specific tool
import retrieveModels from "dedalus-labs-mcp/tools/models/retrieve-models";

// initialize the server and all endpoints
init({ server, endpoints });

// manually start server
const transport = new StdioServerTransport();
await server.connect(transport);

// or initialize your own server with specific tools
const myServer = new McpServer(...);

// define your own endpoint
const myCustomEndpoint = {
  tool: {
    name: 'my_custom_tool',
    description: 'My custom tool',
    inputSchema: zodToJsonSchema(z.object({ a_property: z.string() })),
  },
  handler: async (client: client, args: any) => {
    return { myResponse: 'Hello world!' };
  })
};

// initialize the server with your custom endpoints
init({ server: myServer, endpoints: [retrieveModels, myCustomEndpoint] });

Available Tools

The following tools are available in this MCP server.

Resource models:

• retrieve_models (read): Retrieve a model.

  Retrieve detailed information about a specific model, including its capabilities, provider, and supported features.

  Args: model_id: The ID of the model to retrieve (e.g., 'openai/gpt-4', 'anthropic/claude-3-5-sonnet-20241022') user: Authenticated user obtained from API key validation

  Returns: Model: Information about the requested model

  Raises: HTTPException: - 401 if authentication fails - 404 if model not found or not accessible with current API key - 500 if internal error occurs

  Requires: Valid API key with 'read' scope permission

  Example:

  import dedalus_labs
  
      client = dedalus_labs.Client(api_key="your-api-key")
      model = client.models.retrieve("openai/gpt-4")
  
      print(f"Model: {model.id}")
      print(f"Owner: {model.owned_by}")
      ```
  
      Response:
      ```json
      {
          "id": "openai/gpt-4",
          "object": "model",
          "created": 1687882411,
          "owned_by": "openai"
      }
      ```
  

• list_models (read): List available models.

  Retrieve the complete list of models available to your organization, including models from OpenAI, Anthropic, Google, xAI, Mistral, Fireworks, and DeepSeek.

  Returns: ListModelsResponse: List of available models across all supported providers

Resource embeddings:

• create_embeddings (write): Create embeddings using the configured provider.

Resource audio.speech:

• create_audio_speech (write): Generate speech audio from text.

  Generates audio from the input text using text-to-speech models. Supports multiple voices and output formats including mp3, opus, aac, flac, wav, and pcm.

  Returns streaming audio data that can be saved to a file or streamed directly to users.

Resource audio.transcriptions:

• create_audio_transcriptions (write): Transcribe audio into text.

  Transcribes audio files using OpenAI's Whisper model. Supports multiple audio formats including mp3, mp4, mpeg, mpga, m4a, wav, and webm. Maximum file size is 25 MB.

  Args: file: Audio file to transcribe (required) model: Model ID to use (e.g., "openai/whisper-1") language: ISO-639-1 language code (e.g., "en", "es") - improves accuracy prompt: Optional text to guide the model's style response_format: Format of the output (json, text, srt, verbose_json, vtt) temperature: Sampling temperature between 0 and 1

  Returns: Transcription object with the transcribed text

Resource audio.translations:

• create_audio_translations (write): Translate audio into English.

  Translates audio files in any supported language to English text using OpenAI's Whisper model. Supports the same audio formats as transcription. Maximum file size is 25 MB.

  Args: file: Audio file to translate (required) model: Model ID to use (e.g., "openai/whisper-1") prompt: Optional text to guide the model's style response_format: Format of the output (json, text, srt, verbose_json, vtt) temperature: Sampling temperature between 0 and 1

  Returns: Translation object with the English translation

Resource images:

• create_variation_images (write): Create variations of an image.

  DALL·E 2 only. Upload an image to generate variations.

• edit_images (write): Edit images using inpainting.

  Supports dall-e-2 and gpt-image-1. Upload an image and optionally a mask to indicate which areas to regenerate based on the prompt.

• generate_images (write): Generate images from text prompts.

  Pure image generation models only (DALL-E, GPT Image). For multimodal models like gemini-2.5-flash-image, use /v1/chat/completions.

Resource chat.completions:

• create_chat_completions (write): Create a chat completion.

  Generates a model response for the given conversation and configuration. Supports OpenAI-compatible parameters and provider-specific extensions.

  Headers:

  • Authorization: bearer key for the calling account.
  • Optional BYOK or provider headers if applicable.

  Behavior:

  • If multiple models are supplied, the first one is used, and the agent may hand off to another model.
  • Tools may be invoked on the server or signaled for the client to run.
  • Streaming responses emit incremental deltas; non-streaming returns a single object.
  • Usage metrics are computed when available and returned in the response.

  Responses:

  • 200 OK: JSON completion object with choices, message content, and usage.
  • 400 Bad Request: validation error.
  • 401 Unauthorized: authentication failed.
  • 402 Payment Required or 429 Too Many Requests: quota, balance, or rate limit issue.
  • 500 Internal Server Error: unexpected failure.

  Billing:

  • Token usage metered by the selected model(s).
  • Tool calls and MCP sessions may be billed separately.
  • Streaming is settled after the stream ends via an async task.

  Example (non-streaming HTTP): POST /v1/chat/completions Content-Type: application/json Authorization: Bearer

  { "model": "provider/model-name", "messages": [{"role": "user", "content": "Hello"}] }

  200 OK { "id": "cmpl_123", "object": "chat.completion", "choices": [ {"index": 0, "message": {"role": "assistant", "content": "Hi there!"}, "finish_reason": "stop"} ], "usage": {"prompt_tokens": 3, "completion_tokens": 4, "total_tokens": 7} }

  Example (streaming over SSE): POST /v1/chat/completions Accept: text/event-stream

  data: {"id":"cmpl_123","choices":[{"index":0,"delta":{"content":"Hi"}}]} data: {"id":"cmpl_123","choices":[{"index":0,"delta":{"content":" there!"}}]} data: [DONE]