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Apiany Mcp

Apiany Mcp

Local
apiany.aiMedia & Design0Updated 18w ago

About Apiany Mcp

Apiany Mcp is an MCP server and agent skill for developers and agents working with APIAny. It combines model discovery, pricing estimates, documentation context, and integration examples, with paid media task creation gated by explicit confirmation.

Key features

  1. 01Search and inspect APIAny models
  2. 02Estimate credits from pricing data
  3. 03Retrieve API documentation context
  4. 04Generate examples in six languages
  5. 05Create confirmed image and video tasks

Use cases

  1. 01Compare model capabilities and costs
  2. 02Prepare APIAny integration code
  3. 03Check estimated credits before a workflow
  4. 04Submit and track confirmed media tasks

MCP support

Exposes APIAny model lookup, pricing, documentation, usage examples, and media-task tools to MCP clients. Read-only tools need no API key; paid generation tools require an API key and explicit confirmation.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"ApiAny.AI-MCP": {
"command": "npx",
"args": [
"-y",
"@apiany-ai/mcp"
],
"env": {
"APIANY_BASE_URL": "<APIANY_BASE_URL>",
"APIANY_API_KEY": "<APIANY_API_KEY>"
}
}
}
}
Run commandnpx -y @apiany-ai/mcp
Environment variables to set
APIANY_BASE_URLAPIAny base URL. Defaults to https://apiany.ai.
APIANY_API_KEYSecretOptional APIAny API key for paid image/video task tools and task polling.

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@apiany-ai/mcp0.1.2

Tools

10

Saved tool list · updated 3w ago

ParameterTypeRequiredDescription
qstringNoOptional model search text.
typeenumNochat | image | video | audio
providerstringNoOptional provider name, such as OpenAI or Google.
limitintegerNo

Details

From the project README

APIAny MCP

Official MCP server for APIAny model discovery, pricing context, documentation lookup, and OpenAI-compatible integration examples.

APIAny connects agents and developers to 100+ AI models for chat, image, video, and multimodal workflows.

npm version npm package release license Node.js MCP server

Quick Start · Visual Setup · MCP Tools · Agent Skill · Development

English | 中文

APIAny MCP overview

What This Repository Provides

APIAny MCP gives AI agents a structured way to understand and use APIAny:

  • Discover public APIAny models by provider, modality, capability, and pricing metadata.
  • Estimate APIAny credits before choosing a model or running a workflow.
  • Fetch compact documentation context from APIAny docs and llms.txt.
  • Generate OpenAI-compatible cURL, Python, JavaScript, Go, Java, and PHP examples.
  • Create image or video generation tasks only when an API key is configured and the user explicitly confirms paid usage.
  • Package an agent skill at skills/apiany-integration so supported agents can learn the APIAny workflow.

Quick Start

Run the server directly with npm:

npx -y @apiany-ai/mcp

Or install and run it from this repository:

git clone [email protected]:ailingqu/ApiAny.AI-MCP.git
cd ApiAny.AI-MCP
npm install
npm start

Visual Setup

1. Install and Start the MCP Server

Install APIAny MCP with npx

2. Add APIAny MCP to Your Agent Client

Use this configuration in Claude Code, Cursor, VS Code, or another MCP-compatible client:

APIAny MCP client configuration

{
  "mcpServers": {
    "apiany": {
      "command": "npx",
      "args": ["-y", "@apiany-ai/mcp"],
      "env": {
        "APIANY_BASE_URL": "https://apiany.ai"
      }
    }
  }
}

For paid image or video task tools, also set APIANY_API_KEY:

{
  "APIANY_BASE_URL": "https://apiany.ai",
  "APIANY_API_KEY": "your_apiany_api_key"
}
3. Ask Your Agent to Use APIAny

APIAny MCP tool usage in an agent conversation

Example prompts:

Use APIAny MCP to find an image model for product photos, compare pricing, and show JavaScript integration code.
Use APIAny MCP to estimate credits for 20 gpt-style chat requests with 10k input tokens and 5k output tokens.
Use APIAny MCP to show JavaScript usage for nano-banana-pro and Python usage for veo3-1-fast.

MCP Tools

ToolPurposeAPI key
list_modelsList public APIAny models with pricing and capability metadata.No
search_modelsSearch models by text, provider, modality, or capability.No
get_modelGet one public model by model id or display name.No
estimate_costEstimate credits from public pricing metadata.No
get_model_usageReturn endpoint, payload, async behavior, and language examples for models.No
get_integration_examplesReturn cURL, Python, JavaScript, Go, Java, or PHP examples.No
get_docs_contextReturn compact APIAny documentation context from /llms.txt.No
create_image_taskCreate a paid async image task after explicit confirmation.Yes
create_video_taskCreate a paid async video task after explicit confirmation.Yes
get_task_statusRead async media task status.Yes

Agent Skill

The APIAny Integration skill lives in skills/apiany-integration.

It helps agents:

  • Choose APIAny models by modality, provider, pricing, and capability.
  • Generate examples for chat, image, video, and media workflows.
  • Explain async task creation and polling.
  • Require explicit confirmation before paid generation tasks.

Environment Variables

VariableRequiredDescription
APIANY_BASE_URLNoAPIAny base URL. Defaults to https://apiany.ai.
APIANY_API_KEYOnly for paid toolsAPI key used for paid image/video task creation and task polling.

Safety Model

Read-only tools work without an API key. Paid generation tools require both:

  • APIANY_API_KEY
  • confirm_paid_request=true

The server does not persist API keys. It only reads them from the current process environment.

Development

npm install
npm run check
npx -y @modelcontextprotocol/inspector node src/server.js

Useful files:

Links

License

MIT

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.