SuperiorAPIs

SuperiorAPIs

cteaminfo

Connects AI systems to external APIs through structured tools, enabling AI assistants to make calls to third-party services.

Provides a bridge between AI systems and external APIs, enabling structured communication with third-party services through a set of callable tools built on the fastmcp framework.

284 views1Local (stdio)

What it does

  • Bridge AI systems to external APIs
  • Execute structured API calls
  • Handle third-party service communication
  • Process API responses for AI consumption

Best for

AI developers building agents that need external dataIntegrating AI assistants with existing servicesCreating AI workflows that interact with APIs
Built on fastmcp frameworkStructured communication layer

About SuperiorAPIs

SuperiorAPIs is a community-built MCP server published by cteaminfo that provides AI assistants with tools and capabilities via the Model Context Protocol. SuperiorAPIs connects AI systems with third-party APIs like Stripe and LinkedIn for seamless API to API integration and It is categorized under developer tools.

How to install

You can install SuperiorAPIs in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

License

SuperiorAPIs is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

MCP SuperiorAPIs Local

This project is a Python-based MCP Server that dynamically retrieves plugin definitions from SuperiorAPIs and auto-generates MCP tool functions based on their OpenAPI schemas.

It operates in stdio mode, making it ideal for local development and testing with AI clients.

If you need to integrate using HTTP or SSE protocols, please refer to: CTeaminfo/mcp_superiorapis_remote

πŸ“‚ Project Structure

mcp_superiorapis_local/
β”œβ”€β”€ src/mcp_superiorapis_local/     # Main program
β”‚   β”œβ”€β”€ __init__.py           # Package initialization
β”‚   └── server.py             # MCP server implementation
β”œβ”€β”€ tests/                    # Test files
β”œβ”€β”€ pyproject.toml            # Project config & dependencies
β”œβ”€β”€ uv.lock                   # Locked dependencies
└── README.md                 # Project documentation (this file)

πŸš€ Quick Start

1. Environment Preparation

Prerequisites:

2. Clone the Project

# Using HTTPS
git clone https://github.com/CTeaminfo/mcp_superiorapis_local.git

# Using SSH
git clone [email protected]:CTeaminfo/mcp_superiorapis_local.git
cd mcp_superiorapis_local

3. Install uv (if not installed)

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# Or use pip
pip install uv

4. Install Dependencies

# Create virtual environment
uv venv --python 3.13

# Install dependencies
uv sync

# Or use pip
pip install -e .

5. Configure Environment Variables

# Set your Superior APIs token
export TOKEN=your_superior_apis_token_here

# Windows CMD
set TOKEN=your_superior_apis_token_here

Token Authentication Instructions:

  • Get your token from Superior APIs
  • Set the TOKEN environment variable before running the server

6. Start the Server


python -m mcp_superiorapis_local

or

python src/mcp_superiorapis_local/server.py

7. Verify Deployment

The server will:

  1. Fetch plugin data from SuperiorAPIs
  2. Dynamically generate MCP tool functions
  3. Register the tools
  4. Start the MCP server in stdio mode

πŸ”Œ MCP Client Integration

With uvx on Pip

Configure MCP server with uvx on pip(No need to download source code):

{
  "mcpServers": {
    "mcp_superiorapis_local": {
      "command": "uvx",
      "args": [
        "mcp-superiorapis" // https://pypi.org/project/mcp-superiorapis/
      ],
      "env": {
        "TOKEN": "your_superior_apis_token_here"
      }
    }
  }
}

Local Mode

{
  "mcp_superiorapis_local": {
    "command": "uv",
    "args": [
      "run",
      "--directory",
      "/path/to/mcp_superiorapis_local",
      "python",
      "-m",
      "mcp_superiorapis_local"
    ],
    "env": {
      "TOKEN": "your_superior_apis_token_here"
    }
  }
}

πŸ”§ Startup Steps

# 1. Navigate to the project directory
cd mcp_superiorapis_local

# 2. Activate the virtual environment
.venv\Scripts\activate

# 3. Set environment variable
set TOKEN=your_superior_apis_token_here

# 4. Run the project
python -m mcp_superiorapis_local

or

python src/mcp_superiorapis_local/server.py

Note:

  • Dependencies only need to be installed once (using pip install -e . or uv sync)
  • After a reboot, you only need to activate the virtual environment and set the environment variable
  • Once the virtual environment is active, the command prompt will show a (venv) prefix

πŸ”— Related Links

MCPHub Certification

This project is officially certified by MCPHub.

View this project on MCPHub: πŸ”— https://mcphub.com/mcp-servers/CTeaminfo/mcp-superiorapis

Alternatives

Related Skills

Browse all skills
ui-design-system

UI design system toolkit for Senior UI Designer including design token generation, component documentation, responsive design calculations, and developer handoff tools. Use for creating design systems, maintaining visual consistency, and facilitating design-dev collaboration.

18
ai-sdk

Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, Google, etc.), streaming, tool calling, structured output, or embeddings, (4) Use React hooks like useChat or useCompletion. Triggers on: "AI SDK", "Vercel AI SDK", "generateText", "streamText", "add AI to my app", "build an agent", "tool calling", "structured output", "useChat".

6
api-documenter

Master API documentation with OpenAPI 3.1, AI-powered tools, and modern developer experience practices. Create interactive docs, generate SDKs, and build comprehensive developer portals. Use PROACTIVELY for API documentation or developer portal creation.

4
openai-knowledge

Use when working with the OpenAI API (Responses API) or OpenAI platform features (tools, streaming, Realtime API, auth, models, rate limits, MCP) and you need authoritative, up-to-date documentation (schemas, examples, limits, edge cases). Prefer the OpenAI Developer Documentation MCP server tools when available; otherwise guide the user to enable `openaiDeveloperDocs`.

4
cli-builder

Guide for building TypeScript CLIs with Bun. Use when creating command-line tools, adding subcommands to existing CLIs, or building developer tooling. Covers argument parsing, subcommand patterns, output formatting, and distribution.

3
ydc-ai-sdk-integration

Integrate Vercel AI SDK applications with You.com tools (web search, AI agent, content extraction). Use when developer mentions AI SDK, Vercel AI SDK, generateText, streamText, or You.com integration with AI SDK.

2