What can you do with this server?

This server enables AI assistants to generate and edit images via the ChongPlus Image API, with secure local API key management. * Setup & API Key: Check saved key with setup_status, securely save a new key with configure_api_key — the key is stored locally and never exposed. * Image Generation: Use generate_image to create 1–4 images from a prompt, with customizable size (default 2048×2048) and output directory; returns absolute local file paths. * Image Editing: Use edit_image to modify an existing local image based on a prompt, with same size and directory options; only local file paths accepted. * Options & Limits: Retrieve supported image sizes and request limits via list_image_options. * Usage Flow: Designed for conversational use — assistant first checks setup, prompts for key if missing, then proceeds with image tasks, reusing the saved key.

How do I use ChongPlus 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., "@ChongPlus Image MCP generate an image of a cat wearing a 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.

ChongPlus Image MCP

by Rodert

Overview Schema Related Servers Score Discussions

Python

Local

ChongPlus Image MCP

An MCP server that lets AI assistants generate and edit images through the ChongPlus Image API.

It is designed for normal chat usage: after installation, the assistant checks whether a key is configured, asks for one only when necessary, and saves it in the user's local configuration directory. Users do not need to set environment variables or run Python commands.

What the assistant can do

Check whether ChongPlus is ready to use.
Save or replace a ChongPlus API key securely.
Generate one to four images from a prompt.
Edit one local reference image with a prompt.
Explain available image sizes and limits.

Generated images are saved locally. The tools return their absolute paths, so an MCP client can show or open them.

Related MCP server: laozhang-image MCP

Install

Paste the following into an MCP-capable AI client:

Please install and configure ChongPlus Image MCP:
https://github.com/Rodert/chongplus-image-mcp

Install it as a local stdio MCP server using:
uvx --from git+https://github.com/Rodert/chongplus-image-mcp chongplus-image-mcp

After configuration, call setup_status to check the API key. If it is not configured, ask me for my ChongPlus API key and call configure_api_key to save it securely in the local configuration. Reuse the saved key afterwards and do not ask me to configure environment variables.

For manual configuration, use uvx:

{
  "mcpServers": {
    "chongplus-image": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/Rodert/chongplus-image-mcp", "chongplus-image-mcp"]
    }
  }
}

For a checked-out repository, replace the command and arguments with:

{
  "command": "uv",
  "args": ["run", "--directory", "/absolute/path/to/chongplus-image-mcp", "chongplus-image-mcp"]
}

First use

Ask the assistant to generate or edit an image. It should call setup_status first. If no key exists, it asks you for a ChongPlus API key and calls configure_api_key.

Get a key from https://api.chongplus.plus/keys; choose the image-generation group. Treat this as sensitive information: only provide it to an AI client and local MCP server you trust.

The key is stored at:

Platform	Location
macOS/Linux	`$XDG_CONFIG_HOME/chongplus-image/config.json`, or `~/.config/chongplus-image/config.json`
Windows	`%APPDATA%\\chongplus-image\\config.json`

On macOS and Linux, the directory is set to 0700 and the file to 0600.

Tools

Tool	Purpose
`setup_status`	Returns whether a local API key has been saved.
`configure_api_key`	Securely saves a supplied API key. The server never returns it.
`generate_image`	Generates images from a prompt.
`edit_image`	Edits a local image using a prompt.
`list_image_options`	Lists supported sizes and request limits.

The server accepts existing local files for editing. It does not accept URLs, and it never returns file contents from arbitrary local paths.

Development

Requires Python 3.10 or newer and uv.

uv sync
uv run python -m unittest discover -s tests
uv run chongplus-image-mcp

Tests are offline and do not require an API key.

Errors

401 or 403: the API key may be invalid, lack image-model access, or have no remaining quota.
403 with Cloudflare 1010: ChongPlus's edge firewall blocked the request before the API processed it. Contact ChongPlus support with the approximate request time.
Connection errors: check your internet connection and retry.

The API source of truth is https://api.chongplus.plus/tools/image-studio/docs/.

Available Tools

5 tools

configure_api_keyB

Save a user's ChongPlus API key locally. api_key is sensitive: never repeat it in chat, logs, or tool results.

ParametersJSON Schema

Name	Required	Description	Default
`api_key`	Yes

TDQS

B3.4/5.0

Behavior3/5

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

Highlights sensitivity and prohibition of repeating key, but no other behavioral traits (e.g., overwrite behavior, persistence).

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?

Two concise sentences, front-loaded with purpose and critical warning. No unnecessary words.

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?

Lacks details on outcome, state changes, or error handling. With no schema coverage or annotations, description is too sparse.

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 coverage 0%; description only notes sensitivity, not format, constraints, or validation rules for api_key.

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?

Clear verb (save) and resource (ChongPlus API key). Distinguishes from siblings like generate_image and setup_status.

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?

No explicit when-to-use or alternative tools. Implicitly it's for initial setup, but lacks guidance.

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

edit_imageB

Edit one existing local image with a prompt and save the result locally. image_path must be a local file path, not a URL.

ParametersJSON Schema

Name	Required	Default
`size`	No	2048x2048
`count`	No
`prompt`	Yes
`image_path`	Yes
`output_directory`	No

TDQS

B3.3/5.0

Behavior2/5

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

No annotations provided, so description must carry full burden. It mentions image_path must be a local file path, but does not disclose what happens to the original file, output naming, or defaults for parameters like output_directory. Lacks details on side effects or failure modes.

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?

Two sentences, front-loaded with purpose, no unnecessary words. The second sentence adds a critical constraint. All sentences earn their place.

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 5 parameters, no output schema, and no annotations, the description is insufficient. It does not explain how parameters like count (multiple edits?) or output_directory work, nor what the tool returns or saves.

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 0%, so description needs to add meaning. Only image_path constraint is clarified; other parameters (size, count, prompt, output_directory) are not explained. The default values and their implications remain unclear.

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 edits an existing local image with a prompt and saves locally. It uses specific verbs and resources and distinguishes from sibling tools like generate_image by emphasizing 'existing local image'.

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 editing existing images (not generating new ones), but does not explicitly state when to use or avoid this tool, nor mention alternatives like generate_image or list_image_options.

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

generate_imageB

Generate one to four images from a prompt and save them locally. Returns absolute image paths.

ParametersJSON Schema

Name	Required	Default
`size`	No	2048x2048
`count`	No
`prompt`	Yes
`output_directory`	No

TDQS

B3.1/5.0

Behavior3/5

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

Discloses saving locally and returning absolute paths, but no annotations are provided to cover safety, destructive intent, or rate limits. The description carries the full burden but omits details like potential overwriting, API usage costs, or required authentication.

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?

Two concise sentences with the action front-loaded. No unnecessary words. Perfectly scoped for the information provided.

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 4 parameters, no output schema, no annotations, and sibling tools with related functionality, the description is too minimal. It fails to specify constraints like the valid range for 'count' or the format for 'size'. The return value is mentioned but not structured.

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 0% (no parameter descriptions in schema). The tool description does not explain any parameter meanings beyond their names. For example, it doesn't clarify that 'size' expects a format like '2048x2048' or that 'count' must be between 1 and 4.

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 'generate' on resource 'images' with specific details (1-4 images from a prompt, save locally, return paths). It distinguishes well from sibling tools like list_image_options (listing) and edit_image (editing).

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 such as edit_image or list_image_options. The description does not mention contexts, prerequisites, or exclusions.

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

list_image_optionsA

List ChongPlus image generation limits. Use this to choose a supported image size or count.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A4.4/5.0

Behavior4/5

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

No annotations, but description indicates a read-only listing operation. Does not detail potential side effects or auth needs, but for a simple list tool it is adequate.

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?

Two concise sentences, front-loaded with the verb, no filler. Every sentence adds value.

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?

Lacks detail on output format (e.g., array, object), but adequately describes the tool's function for a simple parameterless query. No output schema to rely on.

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

Parameters4/5

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

No parameters (schema coverage 100%). Baseline 4. Description adds context about what the options relate to (size/count).

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?

Clearly states it lists ChongPlus image generation limits, and explains usage for choosing sizes/counts. Distinguishes from siblings like generate_image or edit_image.

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

Usage Guidelines4/5

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

Implies use before generating images to pick supported values. Does not explicitly exclude or mention alternatives, but context is clear.

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

setup_statusA

Check whether this computer has a saved ChongPlus API key. Call this before image generation when setup is uncertain.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A5/5.0

Behavior5/5

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

For a tool with no annotations, the description fully discloses its read-only check behavior without hidden effects.

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?

Single, front-loaded sentence with no wasted words.

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

Completeness5/5

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

Complete for a simple, zero-parameter tool with no output schema; description covers purpose and usage.

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

Parameters5/5

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

No parameters exist, schema coverage is 100%, and the description adds purpose context beyond the schema, meeting the baseline for zero parameters.

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 the tool checks for a saved ChongPlus API key with a specific verb and resource. It distinguishes from siblings like configure_api_key and generate_image.

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

Usage Guidelines5/5

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

Explicitly advises calling this tool before image generation when setup is uncertain, providing clear when-to-use guidance.

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.

5 tool updatesv0.1.0Jul 29, 2026
- First observedconfigure_api_key
- First observededit_image
- First observedgenerate_image
- First observedlist_image_options
- First observedsetup_status

Install Server

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

TDQS

A3.9/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: checking setup, configuring API key, listing options, generating images, and editing images. No overlap exists.

Naming Consistency5/5

Tool names follow a consistent verb_noun pattern (setup_status, configure_api_key, etc.) with underscores, making them predictable and easy to understand.

Tool Count5/5

With 5 tools, the server is well-scoped for image generation, covering setup, configuration, option discovery, generation, and editing without being too sparse or bloated.

Completeness4/5

The tool surface covers the core workflow (setup, config, generate, edit). A minor gap is the absence of a tool to list or manage previously generated images, but this does not severely impact the primary use case.

Maintenance

ActivitySlowing

ResponsivenessNo issues

Resources

GitHub Repository

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