FluxMCP

Server Quality Checklist

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Latest release: v0.1.8
Disambiguation5/5
Each tool has a clearly distinct purpose: generate vs. edit, single vs. batch retrieval, and model/tool discovery. The descriptions explicitly cross-reference to avoid confusion, making the boundaries obvious.
Naming Consistency5/5
All tool names follow a consistent 'flux_<verb>_<noun>' pattern (e.g., flux_generate_image, flux_get_task). There are no mixed conventions or vague verbs, making the naming predictable and readable.
Tool Count5/5
With 6 tools, the server is well-scoped for its image generation and editing purpose. Each tool serves a necessary function, and the count is within the ideal range.
Completeness5/5
The tool set covers the full lifecycle of image generation and editing: create (generate/edit), track (get_task/batch), and discover (list_models/actions). No obvious dead ends or missing core operations.
Average 4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 22 commits in the last 12 weeks
- Last stable release on August 11, 2026
- 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.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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

Behavior2/5
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 behavioral disclosure. The description says it returns 'Task ID and edited image information including URLs,' but does not clarify whether the operation is asynchronous or synchronous, nor does it mention that users may need to poll with flux_get_task or provide a callback_url to receive results. Given the schema includes a callback_url, the async nature is likely but not disclosed, and the wording may mislead an agent into expecting the edited image immediately.
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 well-structured with a clear opening sentence, a brief paragraph on model suitability, bulleted use cases, an alternative reference, and a returns section. It is front-loaded with the core purpose. However, there is some redundancy among the 'Use this when' bullets (e.g., 'modify or transform' and 'change specific elements'), which slightly reduces efficiency.
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 tool has 5 parameters, a rich schema, and an output schema, but the description misses the critical asynchronous workflow context. It does not explain that the operation is likely async, that users may need to poll with get_task or get_tasks_batch, or how the task lifecycle works. This gap is important for an agent to correctly invoke and monitor the edit, despite the schema covering parameter details well.
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 has 100% parameter description coverage, including detailed guidance for model selection, size formatting, and prompt examples. The description adds no additional parameter semantics beyond what the schema already provides, so it earns the baseline score of 3.
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 opens with 'Edit an existing image using Flux with a text prompt,' clearly identifying the verb (edit), resource (existing image), and mechanism (Flux and prompt). It explicitly distinguishes itself from the sibling flux_generate_image by stating 'For generating new images from scratch, use flux_generate_image instead,' making purpose and differentiation unambiguous.
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 provides explicit 'Use this when' bullets covering common editing scenarios (transform, change elements, apply styles, add/remove/replace objects). It also names the alternative tool for new image generation, flux_generate_image, making the decision of when to use this tool versus alternatives very clear.
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?
No annotations are provided, so the description must carry the transparency burden. It does disclose the return value ('Returns: Task ID and generated image information including URLs'), but it does not mention asynchronous behavior, authentication, rate limits, or other operational traits. The return info adds useful context but leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with a clear purpose and has a structured layout (use cases, returns). However, the background paragraph about Flux models and the 'Use this when' bullets contain redundant information that is already evident from the tool name and schema, making it slightly more verbose than necessary.
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 has 5 parameters, an output schema, and a clear sibling context, the description provides sufficient high-level guidance: when to use, what it does, and what it returns. It lacks explicit notes on asynchronous execution or model-specific recommendations, but the schema compensates for most missing details.
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% parameter coverage, including detailed descriptions and examples for prompt, size, model, count, and callback_url. The description adds only generic context ('Different models offer different tradeoffs') and does not enhance understanding beyond what the schema already offers.
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 opens with 'Generate AI images from a text prompt using Flux,' which is a specific verb+resource statement. It further distinguishes itself from sibling tools by ending with 'For editing existing images, use flux_edit_image instead.'
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 provides a 'Use this when' section with three clear use cases, and it names the alternative tool for image editing. This gives direct guidance on when to select this tool versus alternatives.
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?
No annotations provided, so description carries burden. It states 'Efficiently check status' and 'returns status and result information', but does not disclose rate limits, authentication needs, or error behavior—adequate but minimal.
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?
Description is concise, front-loaded with purpose, and uses bullet points for clarity—every sentence adds value without redundancy.
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?
With output schema present and simple tool, description covers usage and return basics. Lacks details on invalid IDs or error handling, but still fairly complete.
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 has 1 parameter with description including max batch size (100% coverage). Description adds context on efficiency and use cases but does not add new parameter meaning, meeting baseline 3.
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 'Query multiple Flux image generation tasks at once' with a specific verb and resource, and it distinguishes itself from the sibling flux_get_task by highlighting batch efficiency.
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?
Provides explicit when-to-use scenarios (multiple pending generations, batch tracking) and mentions efficiency over multiple calls, but lacks when-not-to-use or alternatives beyond the sibling.
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?
No annotations are provided, so the description carries full burden. It correctly indicates this is a read-only query with no destructive effects, and mentions return content (image URLs and metadata). However, it does not disclose any rate limits, caching behavior, or error conditions, which are helpful for a polling tool.
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 well-structured with a clear initial sentence, a bullet list of use cases, and a returns summary. It is concise without being terse, with no redundant information. Minor improvement could be removing the 'Returns:' line if output schema covers it, but still efficient.
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 has one simple parameter and an output schema (context confirms its existence), the description covers the essential purpose and usage context. It explains when to use and what to expect, making it complete for an agent to understand when and how to invoke it.
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?
Schema coverage is 100% with only one parameter (task_id). The schema description is already rich, specifying it comes from generation or edit tools. The description adds context by linking task_id to async workflows. This combination provides sufficient semantic clarity beyond the schema alone.
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 'Query the status and result of a Flux image generation task,' providing a specific verb and resource. It differentiates from sibling tools like flux_get_tasks_batch by focusing on a single task, and from generation tools by being a query operation.
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 includes a 'Use this when' section listing four specific scenarios (check completion, retrieve URLs, async callback, delayed results). While it does not explicitly mention when not to use or alternatives like flux_get_tasks_batch, the guidance is clear and actionable.
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 provided, the description fully bears the burden of disclosure. It adequately describes the behavior: listing models with capabilities and recommendations, implying a read-only, non-destructive operation.
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 concise with three short sentences covering what, why, and return. It is front-loaded with the primary action and adds value without verbosity.
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 no parameters and an output schema, the description is fairly complete. It explains the purpose, return value, and use case, though it could explicitly state it is read-only.
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?
There are no parameters, and schema coverage is 100%. The description adds context by stating the return content (detailed list with descriptions and recommendations), which is not in 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 that the tool lists all available Flux models and their capabilities, with a specific verb (List) and resource (Flux models). This distinguishes it from sibling tools that edit, generate, or retrieve tasks.
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 mentions it is a 'Reference guide for choosing the right Flux model for your use case,' implying usage before model-dependent operations, but it does not explicitly exclude other uses or mention alternative tools.
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?
No annotations are provided, so the description bears the full burden. It states the return type ('categorized list of all tools with descriptions') but doesn't disclose behavioral traits like no side effects, idempotency, or performance characteristics. For a list operation, this is adequate but not exemplary.
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?
Three sentences, no waste. The first sentence immediately states the core purpose, the second explains its role, and the third describes the return. Front-loaded and efficient.
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 no parameters and the presence of an output schema, the description sufficiently explains what the tool does and what it returns. It is complete for a simple listing tool.
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 input schema has zero parameters, and the description adds value by confirming that it lists 'all' available tools, implying no filtering options. With 100% schema coverage, the description reinforces the simplicity.
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 'List all available Flux tools and their use cases,' which is a specific verb-resource combination. It distinguishes from sibling tools like flux_generate_image and flux_list_models, 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description frames it as a 'reference guide for what each tool does and when to use it,' implying it should be used to understand other tools. While it doesn't explicitly state when not to use it, the sibling context makes its utility clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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