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guilyx

ASCII Banner MCP Server

by guilyx
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have perfectly distinct purposes: generate_banner creates ASCII art from text, while get_fonts lists available fonts. There is no overlap or ambiguity between them; an agent can easily tell them apart based on their clear, complementary functions.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: generate_banner and get_fonts. The naming is predictable and readable, with no deviations or mixed conventions, making it easy for an agent to understand and use them.

    Tool Count2/5

    With only 2 tools, this server feels thin for its apparent scope of ASCII banner generation. While the tools cover core functionality, the count is too low for a typical server, limiting flexibility and potentially requiring agents to work around missing operations like customization or management features.

    Completeness3/5

    The server covers basic ASCII banner generation with text input and font selection, but there are notable gaps. For example, it lacks tools for customizing banners (e.g., adjusting width, alignment, or colors) or managing generated banners, which could hinder more advanced agent workflows in this domain.

  • Average 4.5/5 across 2 of 2 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 is passing

  • This repository is licensed under MIT License.

  • 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.

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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

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden. It describes the core behavior (generating ASCII art) and mentions the return format, but doesn't cover potential limitations like text length constraints, error conditions, or performance characteristics that would be helpful for an agent.

    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 well-structured with clear sections (Args, Returns), uses bullet-like formatting for font options, and every sentence adds value. It's appropriately sized for a 2-parameter tool with good information density.

    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?

    Given the tool's moderate complexity, no annotations, and the presence of an output schema (which handles return values), the description is mostly complete. It covers purpose, parameters, and basic usage, though could benefit from more behavioral context about limitations or edge cases.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The description adds significant value beyond the input schema, which has 0% description coverage. It explains what 'text' represents with examples, describes the 'font' parameter with default value, common options, and how to discover available fonts using get_fonts(). This fully compensates for the schema's lack of 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's purpose: 'Generate an ASCII art banner from the given string.' It specifies the verb ('Generate'), resource ('ASCII art banner'), and distinguishes from its sibling get_fonts by focusing on banner creation rather than font listing.

    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 provides clear context for usage by mentioning the sibling tool get_fonts() to list available fonts, which helps guide when to use this tool. However, it doesn't explicitly state when not to use it or mention alternatives beyond the font listing reference.

    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 are provided, so the description carries full burden. It discloses the return type ('Returns a list of font identifiers') and the tool's purpose for discovery, which is adequate behavioral context for a read-only list operation. However, it doesn't mention potential limitations like rate limits or error conditions.

    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?

    Three sentences that are front-loaded with the core purpose, followed by usage guidance and examples. Every sentence earns its place with no wasted words, making it highly efficient and well-structured.

    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 the tool's simplicity (0 parameters, no annotations, but has an output schema), the description is complete. It explains what the tool does, how to use it, and references the output format, which is sufficient since the output schema will detail the return values.

    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 tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose and output, which adds value beyond the empty 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 specific verb ('List') and resource ('all available pyfiglet font names'), and distinguishes from its sibling tool generate_banner by explaining the relationship. It provides concrete examples of font identifiers like 'slant', 'standard', 'block'.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

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

    Explicitly states when to use this tool ('Use this to discover which fonts... are available') and provides a clear alternative ('that can be used with generate_banner'), giving complete guidance on tool selection versus its sibling.

    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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