What can you do with this server?

This server provides AI agents with focused access to Runway Aleph video generation via the RunAPI platform, enabling task creation, status monitoring, and pricing checks. * Create video editing tasks (edit_video): Submit a Runway Aleph video editing task with a prompt, source video URL, and aspect ratio (16:9, 9:16, 4:3, 3:4, 1:1, 21:9). Tasks can be run synchronously (wait for result) or asynchronously (receive a task ID and poll later). * Monitor task status (get_task): Fetch the current status and latest result payload — including output URLs — for any previously created task using its task ID. * Check pricing (check_pricing): Query the current pricing snapshot for the Runway Aleph model and its edit_video endpoint — no API key required.

How do I use @runapi.ai/runway-aleph-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@@runapi.ai/runway-aleph-mcp edit my video with a cinematic filter" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

@runapi.ai/runway-aleph-mcp

Official

by runapi-ai

Overview Schema Related Servers Score Discussions

TypeScript

Remote

Why This Package?

@runapi.ai/runway-aleph-mcp is a focused Model Context Protocol server for the Runway Aleph model line on RunAPI. It gives MCP-compatible assistants direct access to 1 endpoint and 1 model variant without loading the full RunAPI catalog.

Use this per-model server when an agent should stay scoped to Runway Aleph. Use @runapi.ai/mcp when one assistant should discover every RunAPI model line.

Related MCP server: @runapi.ai/gemini-omni-mcp

Install

Add it to Claude Code:

claude mcp add runway-aleph -s user -- npx -y @runapi.ai/runway-aleph-mcp

Use project scope when the server should be shared with a repository:

claude mcp add runway-aleph -s project -- npx -y @runapi.ai/runway-aleph-mcp

Codex, Cursor, Windsurf, VS Code, Roo Code, and other MCP hosts can use the same stdio command:

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

check_pricing works before sign-in. For task creation and status polling, ask your assistant to call the login tool. It opens a browser login and saves credentials to ~/.config/runapi/config.json, the same file used by runapi login. Headless and CI hosts can still set RUNAPI_API_KEY before starting the MCP host.

Ready-made examples are in examples/ for Claude, Cursor, Windsurf, VS Code, and Roo Code.

Tools

Tool	Auth	Purpose
`edit_video`	Yes	Create a Runway Aleph edit video task and optionally wait for a terminal status. Returns the task id, status, and output URLs.
`get_task`	Yes	Fetch the current status and latest payload for an existing task.
`check_pricing`	No	Look up current pricing for a Runway Aleph model and endpoint.

Models

Runway Aleph covers 1 model variant across 1 endpoint. Each tool accepts the models listed for it:

Tool	Models
`edit_video`	`runway-aleph`

Model availability can change between releases. Use check_pricing or the Runway Aleph model page for the current catalog view.

Agent Prompts

Ask your assistant in natural language; it can inspect pricing, create the task, and return the task id plus output URLs.

Create a task

Run a Runway Aleph edit video task with RunAPI.

The assistant can call check_pricing, then edit_video, and return the task id, status, and output URLs.

Submit without waiting

Create the task but don't wait for it to finish.

The assistant calls the create tool with wait: false and returns the task id. Check on it later with get_task.

Check pricing before creating

Check current Runway Aleph pricing, then create the task if it matches my request.

The assistant calls check_pricing and can link to the Runway Aleph model page for the canonical catalog entry.

Configuration

The server resolves auth in this order:

RUNAPI_API_KEY environment variable, useful for headless and CI hosts
~/.config/runapi/config.json, created by the MCP login tool or runapi login
No key, which still allows check_pricing

The config file is normally managed by login. A pre-provisioned headless config can use:

{
  "apiKey": "your_runapi_key"
}

Do not commit real API keys.

Links

Resource	URL
Runway Aleph model page	https://runapi.ai/models/runway-aleph
npm package	@runapi.ai/runway-aleph-mcp
GitHub repository	runapi-ai/runway-aleph-mcp
RunAPI MCP overview	runapi.ai/mcp
RunAPI docs	runapi.ai/docs

License

Licensed under the Apache License, Version 2.0.

Available Tools

4 tools

check_pricingC

Look up RunAPI pricing for the runway-aleph model line.

ParametersJSON Schema

Name	Required	Description	Default
`model`	No	Model slug. Defaults to the line's primary model.
`action`	No	Endpoint name. Defaults to the endpoint that offers the model.

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It only states 'look up pricing' without detailing side effects, authentication requirements, rate limits, or any output format. For a tool with zero annotation coverage, this is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no wasted words. It is appropriately sized for a simple lookup tool, though it could benefit from additional context without becoming verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has two optional enum parameters, no output schema, and no annotations. The description covers the basic purpose but omits details like what the 'action' parameter does or what the pricing response contains. Given the simplicity, it is minimally viable but leaves gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, with both parameters fully described in the input schema (enums and descriptions). The description adds minimal value beyond stating the model line, but given high schema coverage, a baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool looks up pricing for a specific model line ('runway-aleph'). The verb 'look up' implies a read operation, and the resource is well-defined. It distinguishes itself from siblings like 'edit_video' and 'get_task' by the specific function of pricing lookup.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to use this tool versus alternatives. While siblings are clearly different tools, the description does not specify context, prerequisites, or exclusion criteria. Agents have no information on appropriate usage scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

edit_videoC

Create a Runway Aleph task on RunAPI (edit video). Returns a task id, status, and output URLs.

ParametersJSON Schema

Name	Required	Description
`seed`	No
`wait`	No	Poll until the task reaches a terminal status.
`model`	No	RunAPI model slug for this model line.
`prompt`	Yes
`watermark`	No
`timeout_ms`	No
`aspect_ratio`	No
`callback_url`	No
`poll_interval_ms`	No
`source_video_url`	Yes
`reference_image_url`	No

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must disclose behavioral traits but only states the return format (task id, status, output URLs). It omits important details such as the operation being asynchronous (evident from wait/poll parameters), potential side effects (initiating a processing job), or any auth/cost implications. This is insufficient for a task-creating tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the primary action and includes the key output behavior. No redundant words or unnecessary detail; it is appropriately sized for the information it conveys.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 11-parameter tool with no annotations and no output schema, the description is too sparse. It does not explain the editing workflow, parameter purposes, or how it fits with sibling tools. The return value mention is helpful but insufficient for a complex tool requiring prompt-based video edits with multiple optional controls.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 18%, and the description provides no additional parameter explanations. The required 'prompt' and 'source_video_url' are not elaborated, nor are optional parameters like aspect_ratio, reference_image_url, or callback_url. The description fails to compensate for the sparse schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the action ('Create a Runway Aleph task') and resource ('on RunAPI'), with the parenthetical 'edit video' clarifying the domain. It clearly distinguishes from siblings like login, get_task, and check_pricing, which serve different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. It does not mention that get_task should be used to check status, nor does it specify prerequisites or scenarios for which this tool is appropriate. The usage is only implied by the tool's name and the mention of 'edit video.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_taskA

Fetch the current status and latest result payload for a runway-aleph task.

ParametersJSON Schema

Name	Required	Description	Default
`action`	No	Asynchronous endpoint the task was created on. Defaults to the line's only asynchronous endpoint.
`task_id`	Yes	Task id returned when the task was created.

TDQS

A3.7/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. The verb 'Fetch' implies a read-only operation, and it specifies what is returned (status and result payload). However, it does not disclose error behaviors, authentication needs, or whether results can be polled repeatedly. This is adequate but lacks richer safety or side-effect context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no filler. It efficiently communicates the core function and includes the key resource ('runway-aleph task') in a compact way.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter tool with no output schema and no annotations, the description gives a basic understanding of the return type (status and payload) but omits details about polling behavior, status interpretation, or connection to sibling tools like edit_video. It is minimally complete but leaves room for ambiguity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% description coverage for both parameters (action and task_id), each with clear schema descriptions. The tool description adds no additional parameter semantics beyond what the schema covers, so the baseline score of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Fetch') and the specific resource ('current status and latest result payload for a runway-aleph task'). This distinguishes it from sibling tools like login, edit_video, and check_pricing, making the purpose immediately clear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for checking on an asynchronous task (likely after using edit_video), but it does not explicitly state when to use this tool versus alternatives or include any exclusionary guidance. No alternatives are named, and the when-to-use context is only inferred.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

loginA

Authenticate RunAPI by opening a browser PKCE login flow and saving the API key to ~/.config/runapi/config.json.

ParametersJSON Schema

Name	Required	Description	Default
`force`	No	Re-run browser login when the current credential comes from the local config file.

TDQS

A4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of behavioral disclosure. It transparently describes the login method, the side effect of saving the API key to a specific file path, and implies user interaction via the browser flow. It does not cover failure modes or existing credentials, but the core behavior is clearly disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that leads with the purpose, then details the method and location. Every word earns its place, and there is no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one optional parameter, no output schema), the description sufficiently covers the essential behavior. It explains what the tool does and where it persists data. It omits details like return values or error cases, but these are not critical for tool selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers 100% of the parameter's description (the 'force' boolean's behavior is fully explained in the schema). The tool description adds no additional parameter context, so the baseline score of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Authenticate'), resource ('RunAPI'), and method (browser PKCE login flow). It also specifies the outcome (saving the API key to a config path), distinguishing it from unrelated sibling tools like edit_video and check_pricing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The usage is implied: the tool is for authenticating RunAPI. However, the description does not explicitly state when to use it versus alternatives, nor does it mention conditions under which it should not be used (e.g., already having valid credentials). This is adequate but not explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

3 tool updatesv0.1.7Aug 5, 2026

Changededit_video7 fields changed
- addedInput schema / properties / callback_url
```
Added value: +{
+  "type": "string"
+}
```
- addedInput schema / properties / prompt / type
```
Added value: +"string"
```
- addedInput schema / properties / reference_image_url
```
Added value: +{
+  "type": "string"
+}
```
- addedInput schema / properties / seed
```
Added value: +{
+  "type": "number"
+}
```
- addedInput schema / properties / source_video_url / type
```
Added value: +"string"
```
- addedInput schema / properties / watermark
```
Added value: +{
+  "type": "string"
+}
```
- addedInput schema / required
```
Added value: +[
+  "prompt",
+  "source_video_url"
+]
```

Changedget_task1 field changed

changedInput schema / properties / action / description

Previous value: -"Endpoint the task was created on. Defaults to the line's only endpoint."New value: +"Asynchronous endpoint the task was created on. Defaults to the line's only asynchronous endpoint."

Addedlogin

3 tool updatesv0.1.0Jun 24, 2026
- First observedcheck_pricing
- First observededit_video
- First observedget_task

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.7/5.0

Disambiguation5/5

Each tool has a clear, distinct purpose: login handles authentication, edit_video initiates the main task, get_task retrieves status/results, and check_pricing provides model pricing. There is no overlap or ambiguity among them.

Naming Consistency5/5

All tool names follow a consistent lowercase_snake_case verb-noun pattern (login, edit_video, get_task, check_pricing). No mixed conventions or deviations exist.

Tool Count5/5

Four tools are well-scoped for a narrow API wrapper covering authentication, the primary edit action, status polling, and pricing lookup. Each tool serves a necessary function without bloat or redundancy.

Completeness4/5

The core workflow of login, edit_video, and get_task is fully covered, with pricing as a useful extra. Minor gaps exist, such as no cancel or list tasks, but these are not critical for the primary use case.

Maintenance

ActivitySlowing

ResponsivenessSyncing

Resources

Need Help?

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Looking for Admin?

If you are the server author, to access and configure the admin panel.

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Why This Package?

Install

Tools

Models

Agent Prompts

Create a task

Submit without waiting

Check pricing before creating

Configuration

Links

License

Available Tools

Tool Schema Changelog

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

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