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guilyx

ASCII Banner MCP Server

generate_banner

Create ASCII art banners from text strings using various pyfiglet fonts. Convert plain text into stylized banner displays for terminal applications and documentation.

Instructions

Generate an ASCII art banner from the given string.

Args:
    text: The string to render as ASCII art (e.g. "Hello", "MCP").
    font: Name of the pyfiglet font. Use get_fonts() to list available fonts.
          Default is "standard". Common options: "slant", "block", "bubble", "big".

Returns:
    The ASCII art banner as a multi-line string.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
fontNostandard

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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.

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