Tyk Dashboard MCP Server

What does “Tyk Dashboard” MCP Server do?

The Tyk Dashboard MCP Server is a dynamic tool designed to transform OpenAPI or Swagger specifications into accessible MCP (Model Context Protocol) servers. By doing so, it allows AI assistants to interact directly with REST APIs, making API endpoints available as tools for enhanced developer workflows. The Tyk Dashboard MCP Server enables seamless integration of external APIs with AI-powered clients, providing automated support for tasks such as API requests, authentication, and parameter handling. Its dynamic loading capabilities, support for overlays, and customizable mappings make it ideal for exposing any RESTful API to LLM-powered agents. Developers benefit by making their APIs immediately accessible for querying, file management, and other automated actions, streamlining integration and reducing manual overhead.

List of Prompts

No information about reusable prompt templates provided in the repository or documentation.

List of Resources

No explicit resources (as MCP resources) are listed in the available documentation or codebase.

List of Tools

• Dynamic OpenAPI Operations as Tools
  The server automatically exposes each operation defined in a loaded OpenAPI specification as an MCP tool. Each REST API endpoint (e.g., GET, POST, PUT, DELETE routes) becomes an AI-accessible function, with full support for parameters, authentication, and operation metadata.

Use Cases of this MCP Server

• API Integration for AI Assistants
  Instantly expose any OpenAPI-compatible API to LLM-powered agents for querying, updating, or managing external data sources.
• Rapid Prototyping of API-Driven Workflows
  Enable developers to quickly test and iterate on workflows involving external APIs by making endpoints available as configurable tools within AI environments.
• Automated API Testing
  Use LLMs to automate and validate API requests, responses, and authentication flows through the MCP server.
• Custom API Tooling for Internal Teams
  Provide internal users or teams with branded, AI-accessible versions of corporate APIs for automation, reporting, or management.
• Standardized AI-API Interfaces
  Transform API endpoints into standardized, discoverable tools that can be reused across multiple LLM agents or development projects.

How to set it up

Windsurf

No setup instructions for Windsurf provided.

Claude

1. Ensure you have Node.js installed on your computer.
2. Open Claude Desktop and navigate to Settings > Developer.
3. Edit or create the configuration file:
   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %APPDATA%\Claude\claude_desktop_config.json
4. Add this configuration (customize as needed):

{
  "mcpServers": {
    "api-tools": {
      "command": "npx",
      "args": [
        "-y",
        "@tyktechnologies/api-to-mcp",
        "--spec",
        "https://petstore3.swagger.io/api/v3/openapi.json"
      ],
      "enabled": true
    }
  }
}

5. Restart Claude Desktop.
6. You should now see a hammer icon in the chat input for API tools.

Cursor

No setup instructions for Cursor provided.

Cline

No setup instructions for Cline provided.

Securing API Keys

While the server supports passing custom HTTP headers via environment variables and CLI, there is no explicit example for securing API keys in the configuration. Users should ensure sensitive keys are loaded via environment variables in their system or deployment configuration.

Example (conceptual):

{
  "mcpServers": {
    "api-tools": {
      "env": {
        "API_KEY": "your_api_key"
      },
      "inputs": {
        "header": "Authorization: Bearer ${API_KEY}"
      }
    }
  }
}

Note: Adapt this according to your environment and security policies.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "MCP-name": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “MCP-name” to whatever the actual name of your MCP server is (e.g., “github-mcp”, “weather-api”, etc.) and replace the URL with your own MCP server URL.

Overview

Roots support: The presence of a .roo directory suggests root boundaries may be supported, but it is not explicitly documented.

Based on the two tables, the Tyk Dashboard MCP Server provides a robust way to turn OpenAPI endpoints into AI-usable tools. However, it lacks documentation/examples for prompt templates, explicit MCP resources, and details for some platforms. Sampling support and roots are not clearly addressed. Overall, this MCP server scores well for tool coverage and licensing, but could improve in documentation and feature breadth.

MCP Score

RATING: 6/10