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runapi-ai

@runapi.ai/gpt-4o-image-mcp

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by runapi-ai

Server Quality Checklist

67%
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  • Latest release: v0.1.8

  • Disambiguation5/5

    Each tool has a distinct purpose: login for authentication, text_to_image for creating image tasks, get_task for retrieving task status, and check_pricing for pricing information. No overlap.

    Naming Consistency4/5

    All names use lowercase underscores, but patterns vary: login is a single verb, while others follow verb_noun (get_task, check_pricing) or noun_noun (text_to_image). Minor inconsistency but still readable.

    Tool Count5/5

    Four tools is well-scoped for a focused image generation API, covering authentication, task creation, status retrieval, and pricing without redundancy.

    Completeness4/5

    Covers the core workflow: login, generate, retrieve result, and check pricing. A minor gap is lack of a delete or list tasks tool, but the essential operations are present.

  • Average 3.5/5 across 4 of 4 tools scored. Lowest: 2.9/5.

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

    • No community issues in the last 6 months
    • 12 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?

    No annotations provided. Description only mentions return values (task id, status, output URLs), but lacks disclosure of rate limits, authentication requirements, or potential side effects. Incomplete for a tool with 11 parameters and no annotations.

    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?

    Single sentence is efficient and front-loaded with the key action. However, some additional brevity could be sacrificed for more useful detail without becoming bloated.

    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 11 parameters, no output schema, and no annotations, the description is insufficient. It does not explain parameter relationships, task lifecycle, or error handling, leaving agent without necessary context.

    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 only 18%, meaning most parameters lack schema descriptions. The tool description does not compensate by explaining parameters. Agent must infer meaning from names alone, which may be insufficient.

    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 action 'Create a GPT-4o Image task' and specifies 'text to image'. Distinct from sibling tools (login, get_task, check_pricing) which are about authentication, task retrieval, and pricing.

    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. Does not mention prerequisites or context. Agent has no hints about optimal usage.

    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 disclose behavioral traits. It only states the purpose, without mentioning whether the operation is read-only, requires authentication, has rate limits, or any other behavioral details. This is insufficient for a tool without annotations.

    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 sentence that is concise, front-loaded with the action, and contains no unnecessary words. It efficiently delivers the core purpose.

    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 that there is no output schema and the tool is simple, the description should explain what the return value (pricing info) entails. It does not, leaving the agent without full context. The description is too minimal for complete understanding.

    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 describes both parameters with enums and descriptions, achieving 100% coverage. The description adds no additional parameter meaning, so baseline 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 action ('look up') and resource ('RunAPI pricing') and specifies the model line ('gpt-4o-image'). It effectively distinguishes from sibling tools 'get_task' and 'text_to_image', which serve 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 Guidelines3/5

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

    The description implies usage for pricing queries, but does not explicitly state when to use this tool versus alternatives or provide guidelines on prerequisites or exclusions. The context is implied by the name and sibling differentiation.

    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 must cover behavioral traits. It states the tool fetches status and payload, implying read-only behavior, but does not explain idempotency, error handling, or rate limiting. Adequate for a simple fetch but lacks depth.

    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 concise sentence with no fluff. It front-loads the action and resource, and every word adds value.

    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 the absence of an output schema, the description should explain the return structure in more detail. It only mentions 'status and latest result payload' without specifics. Error conditions and response shape are not addressed, making it incomplete for a fetch operation.

    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 adds context about the task type ('gpt-4o-image') but does not enhance parameter meaning beyond the schema. Baseline score 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 specifies the verb 'Fetch' and the resource 'current status and latest result payload for a gpt-4o-image task'. It distinguishes from sibling tools like text_to_image which creates tasks, and login/check_pricing which are unrelated.

    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 after creating a task via text_to_image, but does not explicitly state when to use or when not to. No mention of alternatives or prerequisites. Usage context is clear but not fully guided.

    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?

    No annotations provided, but description discloses the login flow (PKCE), side effect (opening browser), and persistence (saving to config file). Lacks details on failure modes or existing credential handling.

    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, concise. Every word 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?

    Adequate for a simple login tool with one optional parameter and no output schema. Covers purpose, mechanism, and side effect. Missing guidance on return values but acceptable.

    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%, baseline 3. Description adds no extra meaning beyond schema for the 'force' parameter.

    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, saving API key). It distinguishes from unrelated siblings.

    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/when-not or alternatives mentioned. Siblings are unrelated, so usage is implied but could be more explicit about prerequisite for other tools.

    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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