FluxMCP

Server Quality Checklist

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Latest release: v0.1.11
Disambiguation5/5
Each tool targets a distinct operation: generate vs edit, list models vs list tools, and single vs batch task queries. The cross-references in descriptions further reduce any chance of misselection.
Naming Consistency5/5
All tools follow the consistent pattern of `flux_` prefix plus verb_noun (generate_image, list_models, get_task, etc.). The naming style is uniform and predictable across the entire set.
Tool Count5/5
Six tools is well-scoped for an image generation and editing server, covering the core operations without unnecessary bloat. Each tool adds clear value to the workflow.
Completeness4/5
The core lifecycle of generating/editing images and retrieving results is well covered, along with model discovery. A minor gap is the lack of a task cancellation tool, but this is not essential for typical usage.
Average 4.2/5 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
- 14 commits in the last 12 weeks
- Last stable release on August 28, 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.
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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

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 the full transparency burden. It mentions returning a 'Task ID and generated image information including URLs,' which hints at async/task-based behavior. However, it does not explain whether generation is synchronous, how long it may take, whether it should be polled via flux_get_task, or side effects such as cost/rate limits. Some insight is given, but it is not comprehensive.
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 structure is effective: a one-sentence purpose, brief context, use-case bullets, a sibling-tool contrast, and a returns section. It is slightly wordier than necessary—some model-family background could be trimmed—but every section earns its place.
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 5-parameter image-generation tool, the description provides enough high-level context: generation purpose, model family tradeoff, use cases, editing alternative, and output type. It does not explicitly mention how to monitor task progress or poll until successful generation, but the 'Task ID' return value and the presence of flux_get_task make a workable inference.
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%, so the baseline is 3. The description refers to prompts and mentions high-quality generation, but does not add substantial meaning beyond the schema's parameter descriptions. The schema already documents model recommendations, size formats, count/defaults, and callback_url semantics.
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 a specific verb and resource: 'Generate AI images from a text prompt using Flux.' It clearly distinguishes this tool from flux_edit_image by explicitly stating that editing existing images should use the sibling tool.
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 'Use this when' section lists three concrete scenarios for new image generation, and explicitly states that editing existing images should use flux_edit_image instead. This provides clear when-to-use and 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.
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 carries the full burden. It explains the edit operation and mentions kontext model specifics but doesn't disclose async behavior (callback_url suggests it), rate limits, or auth requirements. Adequate but not rich.
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?
Well-structured with clear sections, front-loaded purpose, and a concise returns line. Slightly long but every sentence adds value for usage guidance.
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?
Covers purpose, usage, alternatives, and parameter guidance. The output schema exists and the return statement is brief; however, missing behavioral details (async, callback semantics) and no explicit when-not-to-use beyond generation, but sufficient for a complex multi-model 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?
Schema coverage is 100% with descriptions for all parameters. The description adds value by elaborating on recommended models and giving prompt examples beyond the schema, though not deeply for other params.
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 states 'Edit an existing image using Flux with a text prompt' with specific verbs and resource. It clearly distinguishes from flux_generate_image by explicitly noting the sibling for generation.
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?
Provides explicit 'Use this when' list with four concrete scenarios and names the alternative tool (flux_generate_image) for when not to use it.
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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