Skip to main content
Glama
htakeda777

Fal.ai OpenAI Image MCP Server

by htakeda777
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'generate_image' has a clearly defined and distinct purpose for generating images via a specific API.

    Naming Consistency5/5

    A single tool inherently has perfect naming consistency as there are no other tools to compare against. The name 'generate_image' follows a clear verb_noun pattern, which would be consistent if more tools were added.

    Tool Count2/5

    A single tool is too few for a server with a domain like image generation, which could reasonably include operations like listing models, editing images, or fetching image details. This minimal set may cause agents to hit dead ends or lack flexibility in handling related tasks.

    Completeness2/5

    The server is severely incomplete for an image generation domain, covering only text-to-image generation. Obvious gaps include no tools for model management, image editing, retrieval of generated images, or error handling, which limits agent workflows and could lead to failures in broader tasks.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits 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.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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. It mentions the API but fails to describe key behavioral traits like rate limits, authentication needs, response format, or potential side effects (e.g., cost implications). This leaves significant gaps for an AI agent to understand how to interact with the tool effectively.

    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 a single, efficient sentence that directly states the tool's function and API. It is front-loaded with the core action and avoids unnecessary details. However, it could be slightly more informative without sacrificing brevity, such as hinting at common use cases.

    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 complexity of an image generation tool with 5 parameters and no output schema, the description is insufficient. It lacks information on what the tool returns (e.g., image URLs, metadata), error handling, or operational constraints. Without annotations or output schema, the description should provide more context to ensure the agent can use the tool correctly.

    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 description does not add any meaning beyond the input schema, which has 100% coverage with detailed descriptions for all parameters. Parameters like 'prompt', 'image_size', and others are well-documented in the schema, so the description's lack of param info is acceptable. The baseline score of 3 reflects adequate schema coverage without extra value from the description.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('Generate images') and specifies the resource/API ('fal.ai GPT image-1 text-to-image API'), making the purpose evident. It lacks differentiation from siblings, but since there are no sibling tools, this is not a drawback. The description is specific enough to understand what the tool does without being tautological.

    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, such as typical use cases, prerequisites, or alternatives. It simply states what the tool does without context for its application. Since there are no sibling tools, the lack of differentiation is not an issue, but overall usage guidance is missing.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

fal_openai_image1 MCP server

Copy to your README.md:

Score Badge

fal_openai_image1 MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/htakeda777/fal_openai_image1'

If you have feedback or need assistance with the MCP directory API, please join our Discord server