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
- 01Search and inspect APIAny models
- 02Estimate credits from pricing data
- 03Retrieve API documentation context
- 04Generate examples in six languages
- 05Create confirmed image and video tasks
Use cases
- 01Compare model capabilities and costs
- 02Prepare APIAny integration code
- 03Check estimated credits before a workflow
- 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.
npx -y @apiany-ai/mcpAPIANY_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.
Tools
10Saved tool list · updated 3w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| q | string | No | Optional model search text. |
| type | enum | No | chat | image | video | audio |
| provider | string | No | Optional provider name, such as OpenAI or Google. |
| limit | integer | No |
Details
From the project READMEAPIAny 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.
Quick Start · Visual Setup · MCP Tools · Agent Skill · Development
English | 中文
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-integrationso 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
2. Add APIAny MCP to Your Agent Client
Use this configuration in Claude Code, Cursor, VS Code, or another MCP-compatible client:
{
"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
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
| Tool | Purpose | API key |
|---|---|---|
list_models | List public APIAny models with pricing and capability metadata. | No |
search_models | Search models by text, provider, modality, or capability. | No |
get_model | Get one public model by model id or display name. | No |
estimate_cost | Estimate credits from public pricing metadata. | No |
get_model_usage | Return endpoint, payload, async behavior, and language examples for models. | No |
get_integration_examples | Return cURL, Python, JavaScript, Go, Java, or PHP examples. | No |
get_docs_context | Return compact APIAny documentation context from /llms.txt. | No |
create_image_task | Create a paid async image task after explicit confirmation. | Yes |
create_video_task | Create a paid async video task after explicit confirmation. | Yes |
get_task_status | Read 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
| Variable | Required | Description |
|---|---|---|
APIANY_BASE_URL | No | APIAny base URL. Defaults to https://apiany.ai. |
APIANY_API_KEY | Only for paid tools | API 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_KEYconfirm_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:
src/server.js: MCP server implementation.examples/mcp.json: client configuration example.docs/distribution.md: release checklist for npm, MCP directories, and skill marketplaces.CHANGELOG.md: release history.
Links
- Website: https://apiany.ai
- GitHub: https://github.com/ailingqu/ApiAny.AI-MCP
- npm: https://www.npmjs.com/package/@apiany-ai/mcp
- Models: https://apiany.ai/models
- Docs context: https://apiany.ai/llms.txt
License
MIT
Comments
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.