APIAny MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| APIANY_API_KEY | No | Optional APIAny API key. Read-only public tools work without it. | |
| APIANY_BASE_URL | No | APIAny site/API base URL. | https://apiany.ai |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_modelsC | List public APIAny models with pricing and capability metadata. |
| search_modelsC | Search APIAny models by natural-language text, provider, modality, or capability. |
| get_modelA | Get one APIAny model by public model id or display name. |
| estimate_costC | Estimate APIAny credits for a model using public pricing metadata. |
| get_integration_examplesB | Return APIAny integration examples for a model and language. |
| get_model_usageC | Return endpoint, payload, async behavior, and language example for one or more APIAny models. |
| create_image_taskC | Create a paid APIAny asynchronous image generation task. Requires APIANY_API_KEY and confirm_paid_request=true. |
| create_video_taskC | Create a paid APIAny asynchronous video generation task. Requires APIANY_API_KEY and confirm_paid_request=true. |
| get_task_statusC | Get an APIAny async generation task status. Requires APIANY_API_KEY. |
| get_docs_contextC | Return compact APIAny documentation context from llms.txt. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 10 tools
All tools have clearly distinct purposes: image/video task creation, cost estimation, documentation retrieval, model lookup, usage examples, task status, listing, and searching. No overlap is evident.
All tool names follow a consistent verb_noun pattern with underscores (e.g., create_image_task, list_models). Verbs like create, get, list, search are used uniformly, and naming conventions are fully consistent.
With 10 tools, the set is well-scoped for an API abstraction layer. It covers discovery (list, search), details (get_model, get_model_usage), cost estimation, task creation (image/video), status checking, and documentation—each tool earns its place.
The tool surface covers key workflows: model discovery, cost estimation, async task creation and status checking. Minor gaps include lack of task cancellation or listing, but these are non-essential for core usage, so it's nearly complete.