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MohsenMousaviEyeline

mcp-fal

by MohsenMousaviEyeline
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Remote

Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a clearly distinct domain: image generation, video generation, and a catch-all for any other model. There is no ambiguity or overlap between them.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case (generate_image, generate_video, run_model), making them predictable and easy to understand.

    Tool Count5/5

    Three tools is an ideal number for this server: two specialized tools for the most common tasks and one flexible tool that covers the remaining 1000+ models. The scope is well-scoped without being overwhelming.

    Completeness4/5

    The specialized tools handle the primary use cases (image and video generation), and run_model fills any gaps. However, dedicated tools for other popular model categories (e.g., audio) could improve discoverability, though run_model covers them.

  • Average 3.9/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

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  • 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

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description must fully convey behavioral traits. It mentions 'low-level' but omits details on authentication, rate limits, error handling, output format, or side effects of running arbitrary models.

    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 two sentences, front-loaded with the core purpose, and every sentence is essential. No extraneous information.

    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 generic model runner with no output schema, the description adequately explains the purpose and where to find model details. It could be improved by noting that outputs vary per model or that results are returned directly.

    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%, with clear descriptions for model_id and input. The description adds no new meaning beyond what the schema already provides, resulting in a 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 clearly states 'Run any Fal.ai model by its model ID with arbitrary input parameters', specifying the verb (run), resource (Fal.ai model), and scope (any model). It distinguishes from siblings generate_image and generate_video by being a general-purpose tool.

    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 explains this is a flexible, low-level tool for all models and directs users to the model catalog for parameters. However, it does not explicitly advise when to prefer sibling tools or mention exclusion criteria.

    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?

    Discloses generation time (1-5 minutes) and that it returns a URL. However, no details on error handling, rate limits, or cost. Since annotations are absent, the description partially fills the gap but could be richer.

    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?

    Extremely concise with three sentences, no redundancy. Each sentence serves a purpose: defining capability, listing models, and noting runtime.

    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?

    Covers basic usage and output (URL) but lacks details on asynchronous behavior (e.g., polling vs. blocking), error states, or limitations beyond time. Adequate but not fully comprehensive.

    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?

    Adds context beyond schema descriptions, such as default model selection based on image_url presence and examples of supported models. The duration parameter is noted as model-dependent, providing valuable usage insight.

    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?

    Clearly states the tool generates videos from text or image input using specific models. It distinguishes itself from siblings by focusing on video generation vs. image generation or general model running.

    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?

    Implied usage based on input type (text vs. image) but no explicit guidance on when to use this tool over generate_image or run_model. Missing when-not-to-use or alternative recommendations.

    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 must disclose behavior. It covers generating one or more images, supported models, and return of URLs. It could mention default model and size, but overall transparency is good. No contradictions.

    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?

    Two sentences, front-loaded with purpose, no extraneous words. Concise 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 6 parameters with full schema coverage, no output schema, but the description explains return type (URLs). All necessary information for an image generation tool is covered.

    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%, so baseline is 3. The description adds context by mentioning specific models (FLUX, Stable Diffusion) which relates to the model_id parameter, but does not add significant semantic value beyond the schema descriptions.

    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 generates images from a text prompt using Fal.ai models. It specifies the return of URLs and distinguishes from siblings like generate_video. The verb 'generate' and resource 'image' are specific and appropriate.

    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 use for text-to-image generation but does not explicitly state when to use this tool over siblings like generate_video or run_model. No exclusion criteria or alternative suggestions are provided.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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