What can you do with this server?

This server provides focused access to GPT-4o Image generation capabilities on RunAPI, allowing AI agents to create, monitor, and price image generation tasks. * Generate images from text (text_to_image): Submit a text-to-image task using the gpt-4o-image model, with options to control aspect ratio (1:1, 3:2, 2:3), output count (1, 2, or 4 images), and whether to wait synchronously for results or return a task ID immediately for later polling. * Poll task status (get_task): Fetch the current status and result payload (including output URLs) for a previously created image generation task using its task ID. * Check pricing (check_pricing): Look up the current pricing snapshot for the gpt-4o-image model and its text_to_image endpoint — no API key required. The server is compatible with any MCP host (Claude Code, Cursor, Windsurf, VS Code, etc.) via stdio configuration.

How do I use @runapi.ai/gpt-4o-image-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/gpt-4o-image-mcp Generate an image of a cat wearing a wizard hat" 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/gpt-4o-image-mcp

Official

by runapi-ai

Overview Schema Source Related Servers Score Discussions Admin

TypeScript

Remote

Why This Package?

@runapi.ai/gpt-4o-image-mcp is a focused Model Context Protocol server for the GPT-4o Image 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 GPT-4o Image. Use @runapi.ai/mcp when one assistant should discover every RunAPI model line.

Related MCP server: @runapi.ai/gpt-image-2-mcp

Install

Add it to Claude Code:

claude mcp add gpt-4o-image -s user -- npx -y @runapi.ai/gpt-4o-image-mcp

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

claude mcp add gpt-4o-image -s project -- npx -y @runapi.ai/gpt-4o-image-mcp

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

{
  "mcpServers": {
    "gpt-4o-image": {
      "command": "npx",
      "args": ["-y", "@runapi.ai/gpt-4o-image-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
`text_to_image`	Yes	Create a GPT-4o Image text to image 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 GPT-4o Image model and endpoint.

Models

GPT-4o Image covers 1 model variant across 1 endpoint. Each tool accepts the models listed for it:

Tool	Models
`text_to_image`	`gpt-4o-image`

Model availability can change between releases. Use check_pricing or the GPT-4o Image 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 GPT-4o Image text to image task with RunAPI.

The assistant can call check_pricing, then text_to_image, 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 GPT-4o Image pricing, then create the task if it matches my request.

The assistant calls check_pricing and can link to the GPT-4o Image 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
GPT-4o Image model page	https://runapi.ai/models/gpt-4o-image
npm package	@runapi.ai/gpt-4o-image-mcp
GitHub repository	runapi-ai/gpt-4o-image-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_pricingB

Look up RunAPI pricing for the gpt-4o-image 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

B3.4/5.0

Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It only states the purpose, without mentioning whether the operation is read-only, requires authentication, has rate limits, or any other behavioral details. This is insufficient for a tool without annotations.

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 sentence that is concise, front-loaded with the action, and contains no unnecessary words. It efficiently delivers the core purpose.

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?

Given that there is no output schema and the tool is simple, the description should explain what the return value (pricing info) entails. It does not, leaving the agent without full context. The description is too minimal for complete understanding.

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 describes both parameters with enums and descriptions, achieving 100% coverage. The description adds no additional parameter meaning, so baseline 3 is appropriate.

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 ('look up') and resource ('RunAPI pricing') and specifies the model line ('gpt-4o-image'). It effectively distinguishes from sibling tools 'get_task' and 'text_to_image', 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 Guidelines3/5

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

The description implies usage for pricing queries, but does not explicitly state when to use this tool versus alternatives or provide guidelines on prerequisites or exclusions. The context is implied by the name and sibling differentiation.

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 gpt-4o-image 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

A3.6/5.0

Behavior3/5

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

No annotations provided, so description must cover behavioral traits. It states the tool fetches status and payload, implying read-only behavior, but does not explain idempotency, error handling, or rate limiting. Adequate for a simple fetch but lacks depth.

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 concise sentence with no fluff. It front-loads the action and resource, and every word adds value.

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?

Given the absence of an output schema, the description should explain the return structure in more detail. It only mentions 'status and latest result payload' without specifics. Error conditions and response shape are not addressed, making it incomplete for a fetch operation.

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 descriptions for both parameters. The description adds context about the task type ('gpt-4o-image') but does not enhance parameter meaning beyond the schema. Baseline score of 3 is appropriate.

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 specifies the verb 'Fetch' and the resource 'current status and latest result payload for a gpt-4o-image task'. It distinguishes from sibling tools like text_to_image which creates tasks, and login/check_pricing which are unrelated.

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 after creating a task via text_to_image, but does not explicitly state when to use or when not to. No mention of alternatives or prerequisites. Usage context is clear but not fully guided.

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 behavioral burden. It discloses the interactive browser flow and the file write side effect (config.json). However, it does not mention that it may overwrite existing credentials or that it could block waiting for user input, though these are implied.

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, well-structured sentence that front-loads the action ('Authenticate RunAPI') and provides necessary details without extraneous 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?

For a simple login tool with one optional parameter and no output schema, the description covers the core purpose and side effect. It lacks an explicit statement that this is a prerequisite for other tools, but that is implied.

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 description coverage is 100% (the only parameter 'force' has a description). The tool description adds no additional meaning about parameters beyond the schema, so the baseline 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'), target resource ('RunAPI'), method ('browser PKCE login flow'), and side effect (saving to config.json). It is distinct from sibling tools, none of which relate to authentication.

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 (to authenticate RunAPI) but does not explicitly say when to run it (e.g., before other RunAPI tools) or when to use the 'force' parameter. Since there are no alternative auth tools among siblings, 'vs alternatives' is not applicable.

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

text_to_imageC

Create a GPT-4o Image task on RunAPI (text to image). Returns a task id, status, and output URLs.

ParametersJSON Schema

Name	Required	Description
`wait`	No	Poll until the task reaches a terminal status.
`model`	No	RunAPI model slug for this model line.
`prompt`	No
`mask_url`	No
`timeout_ms`	No
`aspect_ratio`	Yes
`callback_url`	No
`output_count`	No
`poll_interval_ms`	No
`source_image_urls`	No
`enable_prompt_expansion`	No

TDQS

C2.9/5.0

Behavior2/5

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

No annotations provided. Description only mentions return values (task id, status, output URLs), but lacks disclosure of rate limits, authentication requirements, or potential side effects. Incomplete for a tool with 11 parameters and no annotations.

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?

Single sentence is efficient and front-loaded with the key action. However, some additional brevity could be sacrificed for more useful detail without becoming bloated.

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?

Given 11 parameters, no output schema, and no annotations, the description is insufficient. It does not explain parameter relationships, task lifecycle, or error handling, leaving agent without necessary context.

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

Parameters2/5

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

Schema description coverage is only 18%, meaning most parameters lack schema descriptions. The tool description does not compensate by explaining parameters. Agent must infer meaning from names alone, which may be insufficient.

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?

Description clearly states action 'Create a GPT-4o Image task' and specifies 'text to image'. Distinct from sibling tools (login, get_task, check_pricing) which are about authentication, task retrieval, and pricing.

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 on when to use this tool versus alternatives. Does not mention prerequisites or context. Agent has no hints about optimal usage.

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.

2 tool updatesv0.1.7Jul 22, 2026

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."

Changedtext_to_image5 fields changed
- addedInput schema / properties / callback_url
```
Added value: +{
+  "type": "string"
+}
```
- addedInput schema / properties / enable_prompt_expansion
```
Added value: +{
+  "type": "boolean"
+}
```
- addedInput schema / properties / mask_url
```
Added value: +{
+  "type": "string"
+}
```
- addedInput schema / properties / prompt
```
Added value: +{
+  "type": "string"
+}
```
- addedInput schema / properties / source_image_urls
```
Added value: +{
+  "items": {},
+  "maxItems": 5,
+  "type": "array"
+}
```

1 tool updatev0.1.6Jul 13, 2026
- Addedlogin
3 tool updatesv0.1.0Jun 24, 2026
- First observedcheck_pricing
- First observedget_task
- First observedtext_to_image

Install Server

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

TDQS

A3.7/5.0

Scored 2026-07-22 17:47 across 4 tools

Disambiguation5/5

Each tool has a distinct purpose: login for authentication, text_to_image for creating image tasks, get_task for retrieving task status, and check_pricing for pricing information. No overlap.

Naming Consistency4/5

All names use lowercase underscores, but patterns vary: login is a single verb, while others follow verb_noun (get_task, check_pricing) or noun_noun (text_to_image). Minor inconsistency but still readable.

Tool Count5/5

Four tools is well-scoped for a focused image generation API, covering authentication, task creation, status retrieval, and pricing without redundancy.

Completeness4/5

Covers the core workflow: login, generate, retrieve result, and check pricing. A minor gap is lack of a delete or list tasks tool, but the essential operations are present.

Maintenance

ActivitySlowing

ResponsivenessNo issues

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

Related MCP Servers

@runapi.ai/gpt-image-2-mcpofficial