# runapi-ai/mcp [Health: Active]

**Category:** 🎥 Multimedia Process  
**Repository:** https://github.com/runapi-ai/mcp  
**GitHub Stars:** 55  
**npm Downloads (last month):** 2036  
**Views:** 3  
**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

The runapi-ai/mcp MCP server exposes RunAPI through the Model Context Protocol. It gives an MCP client access to model discovery, endpoint inspection, runtime pricing, prompt examples, authenticated task operations, and account balance information. RunAPI presents a unified API across many models and providers, including image, video, music, audio, text-to-speech, and LLM-related operations described by the catalog.

The package is a client for RunAPI rather than a local inference or media-generation backend. Actual authenticated operations are sent to RunAPI, while discovery features can read from the package’s embedded catalog. This makes it suitable for assistants that need to compare available models and inputs before creating a task.

## How it works

An assistant can begin with `list_models`, `list_actions`, or `search_prompts`. `get_model_info` returns the endpoint, input requirements, constraints, and current runtime pricing for a model slug. If a model is available through multiple endpoints, the service and action can be supplied to disambiguate it. `check_pricing` provides current pricing for a model and action rather than relying on memorized values.

For an authenticated workflow, `create_task` submits a RunAPI operation. It requires a caller-generated idempotency key and can either wait for completion or return immediately. Asynchronous work returned without waiting can be monitored with `get_task`. Reusing the same idempotency key is intended for safely retrying the same logical request; changing the request while reusing the key causes a conflict.

## Setup and configuration

Install the local runapi-ai/mcp MCP server from npm with:

```bash
npx -y @runapi.ai/mcp
```

For manual MCP configuration, use `npx` as the command and `-y`, `@runapi.ai/mcp` as its arguments. The README also documents Claude Code commands that register the server at user or project scope, plus an `init` command for generating configurations for several clients.

Authenticated users can call the `login` tool to open a browser-based PKCE login flow. The resulting credentials are saved in `~/.config/runapi/config.json`, which is shared with the RunAPI CLI. Headless or CI environments can provide `RUNAPI_API_KEY` before starting the MCP host.

A hosted endpoint is also available at `https://mcp.runapi.ai/mcp`. Remote clients do not need Node.js and can use OAuth where supported, or provide a RunAPI API key as a bearer credential. The hosted service exposes the documented business tools; local login is available through the local package.

## Tools and capabilities

The runapi-ai/mcp MCP server provides these capabilities:

- Browse models with optional modality, service, and action filters.
- Inspect model inputs, endpoint support, constraints, and runtime prices.
- Group available actions by output modality.
- Search reusable prompts by text, modality, category, tags, model, or featured status.
- Create tasks synchronously or asynchronously with idempotency protection.
- Retrieve task status and the latest result payload.
- Authenticate through browser login and inspect account balance and spending metrics.

## Limitations and notes

Catalog, pricing, and prompt-search access does not require an API key, but creating tasks, reading task status, and checking account balance do. Pricing is runtime data and should be queried when accuracy matters. A completed task may not be available before the client’s wait deadline; in that case, use `get_task` rather than creating a replacement. If a connection closes after submission, the original task may still be processing, so retry with the same idempotency key only when repeating the same request is intended.

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