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runapi-ai

@runapi.ai/flux-kontext-mcp

by runapi-ai

Server Quality Checklist

67%
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  • Latest release: v0.1.12

  • Disambiguation5/5

    Each tool has a clear, unique purpose: login handles authentication, text_to_image creates tasks, get_task retrieves task status, and check_pricing provides pricing info. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    All tool names use consistent snake_case and follow clear verb-based patterns (e.g., text_to_image, get_task, check_pricing). The naming is predictable and easy to understand.

    Tool Count5/5

    With 4 tools, the server is well-scoped for its purpose: authentication, image generation, task polling, and pricing lookup. This is a focused and efficient set without unnecessary extras.

    Completeness4/5

    The tools cover the essential lifecycle: authenticate, create a task, check status, and get pricing. A minor gap is the lack of a list all tasks tool, which may require users to remember task IDs, but core workflows are supported.

  • Average 3.7/5 across 4 of 4 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 16 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

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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?

    With no annotations, the description carries the full burden. It only says 'look up' implying a read operation, but no details on permissions, side effects, or return behavior are given. The minimal description does not disclose behavioral traits beyond the name.

    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, front-loaded sentence with no extraneous information. Every word contributes to the purpose.

    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 description is minimal and does not explain default parameter behavior or output format. While the schema covers parameters, the tool description lacks completeness for a simple lookup without annotations or output schema.

    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 parameter descriptions already present. The tool description does not add any information about parameters beyond the schema, so a baseline 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 states the tool's purpose: 'Look up RunAPI pricing for the flux-kontext model line.' It specifies the action (look up) and resource (pricing) and distinguishes from siblings like get_task and text_to_image.

    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?

    The description provides no guidance on when to use this tool vs alternatives, nor does it mention prerequisites or context. It merely states what it does, leaving the agent without context for selection.

    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 carries the full burden. It discloses that the tool creates a task and returns outputs, but does not mention destructive potential, authentication needs, rate limits, or scheduling behavior. The description lacks critical behavioral context beyond the basic action.

    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 two sentences, concise and front-loaded with the main purpose. No wasted words, but could be more structured given the tool's complexity (13 parameters).

    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 13 parameters and no output schema. The description does not explain editing mode (source_image_url), safety_tolerance, translation, expansion, or how to use polling parameters. It fails to provide sufficient context for correct usage, especially given the complexity.

    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 85%, and the schema already documents each parameter. The description adds only 'English only' for prompt and a mention of return fields, but does not provide additional meaning beyond what the schema offers. Baseline 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 states it creates a Flux Kontext text-to-image task and returns task id, status, and output URLs. This verb+resource combination distinguishes it from sibling tools like login, get_task, and check_pricing.

    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 guidance on when to use this tool vs alternatives. The sibling tools are very different, so context is clear, but the description does not provide any usage scenarios or exclusions.

    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; description does not state it is a read-only operation or any constraints. 'Fetch' implies non-destructive behavior, but explicit mention would raise confidence.

    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, well-formed sentence with no redundant words. Front-loaded with key action and resource.

    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 simple params and no output schema, the description provides essential info (status + payload) but could specify the response structure or default behavior of the 'action' param.

    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 does not add additional meaning beyond the schema, so baseline score 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 uses a specific verb ('Fetch') and resource ('task'), clearly indicating it retrieves status and result. It distinguishes from sibling tools like text_to_image (creation) and login/check_pricing.

    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 when-not-to-use guidance is given, but it's implied via context from sibling tool names. Could be improved by noting it should be called after creating a task via text_to_image.

    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. It discloses the browser-based login flow and file saving side effect. Missing details like potential failure modes or user interaction requirements, but sufficient for basic understanding.

    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 sentence, front-loaded with purpose. No unnecessary words. Efficient and clear.

    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?

    Low complexity (1 optional param). Description covers purpose and main behavior. Missing details on error handling or output, but adequate given simplicity.

    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 100% coverage with a clear description for the force parameter. The description adds context about when to use force (when credential is from config), which adds value beyond 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 the verb 'authenticate', the resource 'RunAPI', and the method 'PKCE login flow'. It also specifies the side effect of saving the API key to a config file, making it distinct from sibling tools like check_pricing or get_task.

    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 implies when to use (for authentication), but does not explicitly state when not to use or provide alternatives. However, sibling tools are unrelated, so no confusion arises. The force parameter usage is implied but not fully elaborated.

    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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