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BACH-AI-Tools

Lorem Ipsum By Api Ninjas

by BACH-AI-Tools
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
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 with other tools. The tool's purpose is clearly defined as generating lorem ipsum placeholder text, making it distinct by default.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'v1loremipsum' follows a single pattern without any deviations or mixing of conventions.

    Tool Count2/5

    A single tool is too few for most server purposes, as it limits functionality and scope. While it matches the simple domain of generating lorem ipsum text, it feels thin and may not support varied agent workflows effectively.

    Completeness3/5

    The tool covers the basic function of generating lorem ipsum text, but there are notable gaps. For example, it lacks options for customizing text length, format, or style, which are common in similar APIs, limiting its utility for diverse use cases.

  • 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 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

  • 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 that the tool 'Returns' text, implying a read-only operation, but doesn't cover other important aspects like rate limits, authentication needs, error handling, or response format. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

    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 concise and front-loaded, consisting of two sentences that directly state the tool's function and source. There's no unnecessary information, and it efficiently communicates the core purpose without waste. However, it could be slightly more structured by explicitly mentioning key parameters or output details.

    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 lack of annotations and output schema, the description is incomplete. It doesn't explain the return format (e.g., text structure, error responses), rate limits, or other behavioral traits needed for effective use. For a tool with 4 parameters and no structured output information, the description should provide more context to compensate.

    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 adds minimal parameter semantics beyond the input schema, which has 100% coverage. It implies parameters control the output ('one or more paragraphs'), but doesn't explain how parameters like 'max_length' or 'random' affect the result. With high schema coverage, the baseline is 3, as the schema already documents parameters well.

    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 tool's purpose: 'Returns one or more paragraphs of lorem ipsum placeholder text.' It specifies the verb ('Returns') and resource ('lorem ipsum placeholder text'), and mentions the API source ('API Ninjas Lorem Ipsum API endpoint'). However, since there are no sibling tools, it doesn't need to differentiate from alternatives, so it doesn't reach the highest score.

    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, prerequisites, or specific contexts. It simply states what the tool does without indicating appropriate scenarios or limitations. This lack of usage context reduces its helpfulness for an AI agent.

    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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Glama performs regular codebase and documentation scans to:

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

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