GPT Image MCP Server

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.1
Disambiguation4/5
The tools are mostly distinct, but generate_image and generate_reference_thumbnail overlap in functionality; however, the descriptions clearly direct agents to the specialized tool for reference thumbnails. generate_batch is a batch wrapper but is clearly named and doesn't conflict semantically.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case, such as generate_image, optimize_for_platform, analyze_thumbnail, and cleanup_temp_files. This makes the tool set predictable and easy to navigate.
Tool Count5/5
Seven tools cover generation, batch processing, optimization, analysis, prompt suggestions, and maintenance without being excessive or too sparse. The count is well-scoped for an image generation server.
Completeness4/5
The tool surface covers image generation, batch creation, platform optimization, thumbnail analysis, and prompt improvement. A minor gap is the lack of an image editing or retrieval tool, but the core workflow is well-covered and no critical dead ends are present.
Average 4/5 across 7 of 7 tools scored. Lowest: 3.2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of disclosing behavioral traits. However, it only states the purpose and parameters, with no mention of side effects, permissions, rate limits, or whether the operation is read-only.
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 concise and front-loaded with the primary purpose. The parameter list is compact and directly useful, with no redundant or extraneous information.
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?
Although the output schema exists (covering return values), the description lacks usage guidelines and behavioral transparency. For a simple tool, this is an acceptable but incomplete context, as an agent needs guidance on when and how to invoke it safely.
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?
The description adds valuable meaning beyond the schema by listing the allowed values for content_type and clarifying that current_prompt is optional. Since schema description coverage is 0%, this compensation is important and reasonably detailed.
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 function with a specific verb ('Get') and resource ('suggestions for improving image generation prompts'). It is easily distinguishable from sibling tools like generate_image or analyze_thumbnail, which have 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. The description does not mention exclusions, prerequisites, or scenarios where this tool is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It implies deletion of files but does not state that this is destructive, irreversible, or what criteria define 'old'. No side effects or permission requirements are mentioned, leaving significant behavioral ambiguity.
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 filler, making it efficient and front-loaded. It could be slightly more informative, but as a concise statement of purpose, it earns its 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?
The description is too sparse for a destructive cleanup tool. It does not explain what files are affected, the safety profile, when to run it, or any return values (though the output schema exists). The overall context is incomplete for safe and correct usage.
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?
The tool has zero parameters, so the schema provides no parameter details. Per the baseline for 0 params, a score of 4 is appropriate. The description adds no parameter info, but none is needed.
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 ('clean up') and the resource ('old temporary files'). This is a specific verb+resource combo that distinguishes it from the unrelated sibling tools, which all deal with image generation/analysis.
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, prerequisites, or exclusions. The sibling tools are unrelated, so there is no alternative to contrast with, but the description still fails to convey any usage context beyond the basic action.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It mentions analysis and suggestions but doesn't clarify what happens to the image data, whether it's stored, how 'effectiveness' is measured, or any limitations. This leaves significant gaps for an agent deciding whether to invoke the 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 concise and front-loaded with the core purpose, followed by a compact argument list. Every sentence contributes value, and there is no redundant or filler content.
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 an output schema, so return values don't need explanation. However, the description omits important context like whether platform and content_category are optional (schema shows platform has a default and content_category can be null), and it doesn't mention any prerequisites or error scenarios. It's minimally viable but not fully complete.
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?
The schema has 0% description coverage, so the description must compensate, and it does. It clearly explains each parameter: image_data as 'Base64 encoded image data', platform with example values, and content_category with examples. This adds meaningful semantics beyond the bare schema, though it could be more precise about format or optionality.
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 function: 'Analyze a thumbnail's effectiveness and provide improvement suggestions.' This uses a specific verb ('analyze') and resource ('thumbnail's effectiveness'), and distinguishes it from sibling tools like generate_image and optimize_for_platform by focusing on evaluation rather than creation or optimization.
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 when to use the tool—when you need to assess a thumbnail and receive suggestions—but it doesn't explicitly compare it to alternatives like optimize_for_platform. It provides clear context but no exclusions or direct 'when to use' guidance, so it falls short of the ideal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It mentions the concurrency limit range (1-10) but does not describe error handling, return behavior, or other side effects. Some context is provided, but deeper traits are missing.
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 concise and front-loaded with the core purpose. The Args block efficiently documents parameters without redundancy or irrelevant details.
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 an output schema and parameters are well explained, but the description lacks usage guidance relative to siblings and does not address potential batch size limits or failure behavior. It covers the essentials but leaves gaps for an agent to fully understand when to use this tool.
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?
The input schema lacks meaningful descriptions (0% coverage), but the description explicitly explains both parameters: requests (list of generation requests) and max_concurrent (concurrency range 1-10). This adds significant value beyond the schema's type and title information.
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 a specific action ('Generate multiple images at once') and distinguishes it from single-image generation by its batch nature. It identifies the resource (images) and the differentiation from sibling tools like 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for batch generation with different parameters, but it does not explicitly state when to prefer this tool over alternatives like generate_image. No exclusions or alternative references are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden for behavioral disclosure. It does not mention whether the original image is modified, if the operation is destructive, any authentication needs, rate limits, or what the output contains. This is a significant gap for a tool that processes user-provided data.
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 concise: one sentence stating the purpose followed by a structured list of parameters. No filler or redundant information. The purpose is front-loaded, and each parameter gets a brief, informative explanation.
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 4 parameters and 0% schema coverage, the description covers each parameter and the core purpose effectively. The presence of an output schema means return values need not be explained here. However, it lacks any behavioral context such as preconditions or error handling, leaving some gaps for a tool in this complexity range.
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?
The 'Args' section in the description explicitly defines each parameter: image_data as Base64 encoded, target_platform with allowed examples, content_type with example values, and optimization_focus as 'areas to focus optimization on'. This compensates for the schema's 0% description coverage and provides meaningful meaning beyond the raw property names.
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 'Optimize an existing image for a specific platform' with a specific verb (optimize), resource (existing image), and scope (platform). This distinguishes it from sibling tools like generate_image (creating new images) and analyze_thumbnail (analyzing).
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?
The mention of 'existing image' and listing target platforms (youtube, instagram, twitter, facebook, blog) provides clear context for when to use this tool. However, it does not explicitly state alternatives or exclusions, so it doesn't fully satisfy the 'when not to use' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well: it explains the tool generates new images (not edits), details reference_image format restrictions (file path/base64, no Claude image format), and describes creative_mode/layout_freedom behavioral differences. However, it does not disclose potential issues like API costs, rate limits, or error behavior.
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 front-loaded with a clear purpose and sibling differentiation, then follows with a structured Args list. Every sentence adds value, and while long, the length is justified by 17 parameters needing explanation. There is no repetition of schema fields beyond what is necessary.
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?
For a tool with 17 parameters and no annotations, the description is remarkably complete. It covers the tool's purpose, content types, when to use an alternative, and enriches every parameter with semantic detail. The presence of an output schema means return-value documentation is not required, so no major gaps remain.
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?
Schema coverage is 0%, and the description compensates by documenting all 17 parameters in the Args list with clear meanings and allowed values. For example, size is 'auto-selected based on content_type,' quality lists low/medium/high/auto, style provides visual style options, and reference_image includes format constraints. This fully bridges the gap left by the 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 clearly states it generates images using OpenAI gpt-image-1 for YouTube thumbnails, blog images, and social media content, and explicitly says it creates 'completely new images from text descriptions.' It also distinguishes itself from the sibling tool generate_reference_thumbnail by noting that tool is for incorporating reference images, giving it a clear identity.
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?
The description gives explicit guidance for when to use this tool vs. generate_reference_thumbnail: 'For incorporating reference images (especially for YouTube thumbnails with people), use generate_reference_thumbnail instead.' It also implies general use cases. However, it does not mention other sibling tools like optimize_for_platform or generate_batch, so not fully covers all alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses a key behavioral constraint: reference_image cannot use Claude's '[Image #1]' format and must be a file path or base64 data. It also explains the meaning of layout_freedom values (standard, flexible, experimental), adding value beyond the annotations (which are absent).
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 structured with a bolded intro, usage guidance, and an args list. Each arg line is compact yet informative, with no filler. The length is justified by the tool's eight parameters.
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?
Given the tool's complexity (8 parameters, no schema descriptions, no annotations), this description covers purpose, usage, parameter semantics, and constraints. The existence of an output schema means return values need not be explained, so no gaps are apparent.
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?
With 0% schema description coverage, the description compensates by explaining all 8 parameters. For example, it clarifies that reference_image must be a file path or base64, and that layout_freedom maps to branding constraints. It provides examples for topic and lists options for composition_style.
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 identifies the tool as a specialized YouTube thumbnail generator using reference images and predefined layouts, and it distinguishes itself from the general generate_image tool by mentioning the standard thumbnail layout. The 'person on right, text on left, red banner' detail provides concrete context.
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?
The description explicitly states when to use this tool (for YouTube thumbnails with the standard layout) and when to use generate_image instead (other content types or custom layouts), giving clear decision criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![gpt-image-mcp MCP server](https://glama.ai/mcp/servers/labeveryday/gpt-image-mcp/badges/card.svg)](https://glama.ai/mcp/servers/labeveryday/gpt-image-mcp)

Score Badge

Copy to your README.md:

[![gpt-image-mcp MCP server](https://glama.ai/mcp/servers/labeveryday/gpt-image-mcp/badges/score.svg)](https://glama.ai/mcp/servers/labeveryday/gpt-image-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/labeveryday/gpt-image-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server