# runapi-ai/mcp [Health: Active]

**Category:** 🎥 Multimedia Process  
**Repository:** https://github.com/runapi-ai/mcp  
**GitHub Stars:** 56  
**npm Downloads (last month):** 1502  
**Views:** 8  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/runapi-ai-mcp

## Description
Unified AI model API for 130+ models across 18 providers. Browse models, check pricing, create image/video/music/audio tasks, poll results, check balance, and call LLM endpoints. Free catalog tools work without an API key. npx @runapi.ai/mcp

## Tools
Capabilities this server exposes over MCP:

- **list_models** — List RunAPI models from the embedded catalog. Optional filters: modality, service, or action.
- **get_model_info** — Get supported endpoint, current runtime pricing, and input constraints for a RunAPI model slug. Add service and action when the model supports multiple endpoints.
- **list_actions** — List RunAPI endpoint names grouped by output modality.
- **check_pricing** — Return current runtime pricing for a RunAPI model/action pair.
- **search_prompts** — Search RunAPI prompt examples by modality, category, tags, text query, model, or featured status. Free, no API key required.
- **check_balance** — Return the authenticated RunAPI account balance and spending metrics.
- **create_task** — Run a RunAPI operation with a caller-generated idempotency key. Asynchronous operations can optionally poll until completion.
- **get_task** — Fetch the current status and latest payload for an existing RunAPI task.
- **login** — Authenticate RunAPI by opening a browser PKCE login flow and saving the API key to ~/.config/runapi/config.json.

## Claude Desktop Quick Installation
Install path detected from listing signals. Uses `npx` (confidence: high):

```json
"mcpServers": {
  "mcp": {
    "command": "npx",
    "args": ["-y","@runapi.ai/mcp"],
    "env": {
      "RUNAPI_API_KEY": ""
    }
  }
}
```

**Requires environment variables:** `RUNAPI_API_KEY` — the values above are empty placeholders; fill in real credentials before running (see the repository for what each one is for).

## Documentation

## What runapi-ai/mcp MCP server does

runapi-ai/mcp MCP server exposes RunAPI’s model catalog and API operations to MCP-compatible AI clients. The catalog covers image generation, video generation, music creation, text-to-speech, and other audio workflows, along with LLM endpoints. Agents can inspect available models, determine which actions they support, review input requirements, and retrieve runtime pricing before submitting work.

The package is a client for RunAPI rather than a local generation engine. Generated media and other operations are performed by RunAPI, while the MCP layer gives an assistant a consistent interface for discovery, authentication, task submission, and status retrieval.

## How it works

Catalog, action, pricing, and prompt-search tools use an embedded catalog and can run without an API key. This makes it possible to discover model slugs and required parameters before signing in. Runtime pricing is returned for the selected model and action, helping an agent avoid relying on stale model information.

Authenticated operations use RunAPI credentials. The `login` tool opens a browser-based PKCE flow and stores the resulting key in `~/.config/runapi/config.json`; the same shared configuration is used by `runapi login`. Headless environments can provide `RUNAPI_API_KEY` before starting the MCP host.

`create_task` accepts a caller-generated idempotency key. It can wait for an asynchronous operation to finish or return immediately when `wait` is false. When submitted without waiting, the returned task ID can be passed to `get_task` later. Reusing the same idempotency key is intended for retrying the same logical request, not for changing its inputs or creating a separate task.

## Setup and configuration

For a local stdio installation, run the package through npx:

```json
{
  "mcpServers": {
    "runapi": {
      "command": "npx",
      "args": ["-y", "@runapi.ai/mcp"]
    }
  }
}
```

Claude Code users can register it with `claude mcp add runapi -s user -- npx -y @runapi.ai/mcp`. Project scope is available when the configuration should be shared through a repository’s `.mcp.json` file. The package also provides `init` commands for Claude, Cursor, VS Code, Windsurf, and Roo configurations.

Remote clients can connect to `https://mcp.runapi.ai/mcp` without installing Node.js. OAuth can be used when supported by the client, or a RunAPI API key can be configured as the bearer credential. Local login is available with the package; the hosted endpoint exposes the business tools.

## Tools and capabilities

- `list_models` filters the embedded model catalog by modality, service, or action.
- `get_model_info` returns endpoint details, input constraints, and current runtime pricing for a model slug.
- `list_actions` groups available RunAPI actions by output modality.
- `check_pricing` returns runtime pricing for a model, service, and action combination.
- `search_prompts` finds reusable prompt examples using modality, category, tags, text, model, featured status, and pagination.
- `login` starts browser authentication and saves shared local credentials.
- `create_task` submits an authenticated RunAPI operation and can optionally poll for completion.
- `get_task` retrieves the latest status and payload for an existing task.
- `check_balance` reports account balance and spending metrics.

## Limitations and notes

Discovery tools do not require authentication, but task creation, task retrieval, and balance checks do. The server depends on RunAPI for execution and does not provide a local model or generation backend. Asynchronous requests may still be processing after a wait deadline or connection interruption; use `get_task` with the task ID rather than submitting a replacement. Keep the original idempotency key and request inputs when retrying an uncertain submission.

_Full upstream README: https://allmcps.com/mcp/runapi-ai-mcp/readme_