Skip to main content
Glama
elhombrejd

BFL MCP Server

by elhombrejd
OverviewSchemaRelated ServersScoreDiscussions
TypeScript

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: 'edit_image' modifies an existing image, while 'generate_image' creates a new image from scratch. There is no overlap in functionality, making it impossible for an agent to confuse them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern ('edit_image' and 'generate_image'), using the same naming convention and style. The verbs are distinct and appropriately descriptive for their actions.

    Tool Count2/5

    With only two tools, the server feels thin for an image generation/editing domain. It lacks essential operations like retrieving, listing, or deleting images, and does not cover a complete workflow, making it under-scoped for typical use cases.

    Completeness2/5

    The tool surface is severely incomplete for an image-related server. It includes generation and editing but misses critical operations such as retrieving, listing, managing, or deleting images, leaving significant gaps that will hinder agent functionality.

  • Average 3.1/5 across 2 of 2 tools scored.

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

  • 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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the model but doesn't cover critical aspects like rate limits, authentication requirements, potential costs, error conditions, or what the output looks like (e.g., image data format). For a generative tool with zero annotation coverage, this is a significant gap.

    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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with every element contributing essential information.

    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 image generation, no annotations, and no output schema, the description is incomplete. It doesn't address behavioral traits, output format details, or usage context, making it inadequate for an agent to fully understand how to invoke this tool effectively.

    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 schema fully documents all 5 parameters. The description adds no additional parameter semantics beyond what's already in the schema (e.g., it doesn't explain prompt best practices or safety tolerance implications). Baseline 3 is appropriate when the schema does the heavy lifting.

    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 an image') and specifies the model ('FLUX.1 Kontext model'), which distinguishes it from generic image generation. However, it doesn't explicitly differentiate from the sibling 'edit_image' tool, which would require mentioning this is for creation from scratch rather than modification.

    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 versus alternatives like 'edit_image'. It lacks context about appropriate scenarios, prerequisites, or exclusions, leaving the agent to infer usage based solely on the tool name and parameters.

    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 of behavioral disclosure. It mentions the model ('FLUX.1 Kontext') but does not describe key behavioral traits such as whether the tool is read-only or destructive (implied as destructive since it edits images), authentication needs, rate limits, error handling, or output format details. The description is minimal and misses critical operational context for a mutation tool.

    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, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core action ('Edit an existing image') and includes essential context (model and prompt basis). Every part of the sentence contributes value, making it highly concise and well-structured.

    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 tool's complexity (editing images with multiple parameters) and lack of annotations and output schema, the description is incomplete. It does not cover behavioral aspects, output details (e.g., what is returned), error cases, or usage constraints. For a mutation tool with no structured safety or output information, the description should provide more context to be adequately helpful.

    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 schema fully documents all 6 parameters. The description does not add any parameter-specific information beyond what the schema provides (e.g., it doesn't explain prompt formatting, image encoding details, or safety tolerance implications). Baseline score of 3 is appropriate as the schema handles parameter documentation adequately.

    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 verb ('Edit') and resource ('an existing image'), specifying it uses the 'FLUX.1 Kontext model based on a text prompt'. It distinguishes from the sibling tool 'generate_image' by focusing on editing existing images rather than generating new ones, though it could be more explicit about the distinction. It's not a tautology and provides specific action details.

    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 editing images with a text prompt, but does not explicitly state when to use this tool versus alternatives like 'generate_image'. It provides context (editing existing images) but lacks explicit guidance on exclusions or prerequisites, such as when not to use it or what alternatives exist beyond the sibling tool.

    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

bfl_mcp MCP server

Copy to your README.md:

Score Badge

bfl_mcp 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/elhombrejd/bfl_mcp'

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