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nkapila6

mcp-meme-sticky

by nkapila6
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one searches the web for images to create memes, while the other uses pre-existing templates. Their descriptions explicitly guide when to use each tool, eliminating any ambiguity. The separation is logical and well-defined.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'generate_meme_' prefix, and the suffixes ('by_searching', 'from_meme_template') clearly indicate their methods. The naming is uniform and predictable across the set.

    Tool Count4/5

    Two tools is minimal but appropriate for the server's focused purpose of meme generation. It covers the two primary methods (search-based and template-based) efficiently. While slightly thin, it avoids redundancy and each tool earns its place.

    Completeness4/5

    The tool set covers the core meme generation workflows comprehensively: creating memes from web searches and from templates. Minor gaps might include editing or deleting generated memes, but the primary functionality is well-covered for the domain.

  • Average 4.3/5 across 2 of 2 tools scored.

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

    • 0 of 1 community issues answered or closed 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

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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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

  • Behavior4/5

    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 effectively describes content guardrails, rejection criteria, and response behavior for violations (e.g., 'respond with: "I cannot create this meme..."'). However, it lacks details on technical behaviors like rate limits, error handling, or performance characteristics.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is front-loaded with key information but includes verbose content guardrails that could be condensed. While the structure is logical, some sentences (e.g., detailed guardrail lists) could be more concise without losing clarity, making it moderately efficient but not optimal.

    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 no annotations and no output schema, the description provides good context for a tool with 4 parameters and content restrictions. It covers purpose, usage, behavior, and parameters adequately, though it could benefit from more detail on output format or error cases to be fully comprehensive.

    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 already documents all parameters. The description adds minimal value beyond the schema, such as noting that search queries should include 'meme' or 'template' if needed, but does not provide significant additional semantic context. This meets the baseline for high schema coverage.

    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 a meme using an image search on the web.' It specifies the action (generate), resource (meme), and method (image search), and distinguishes it from its sibling tool 'generate_meme_from_meme_template()' by emphasizing web search versus template usage.

    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?

    The description provides explicit usage guidelines: 'ALWAYS PREFER USING AN EXISTING TEMPLATE USING `generate_meme_from_meme_template()`. UNLESS THE USER EXPLICITLY ASKS TO SEARCH.' This clearly states when to use this tool versus the alternative, including a specific condition for its use.

    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 the full burden. It discloses key behavioral traits: content guardrails (rejection criteria and response format), parameter constraints (e.g., 'list is only of 2 elements'), and output behavior ('saved links'). However, it lacks details on error handling, performance, or side effects like file system changes, leaving some gaps in transparency.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is front-loaded with purpose and usage guidelines, but it includes verbose sections like the example and content guardrails that could be streamlined. While informative, some sentences (e.g., detailed rejection criteria) are lengthy and could be more concise without losing clarity, affecting overall efficiency.

    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 complexity (4 parameters, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, parameters, constraints, and behavioral rules. However, it lacks details on the output format beyond 'saved links' (e.g., what type of links, error responses), and does not address potential side effects or performance considerations, leaving minor gaps.

    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?

    Schema description coverage is 75%, with two parameters well-documented in the schema. The description adds significant value beyond the schema: it provides an extensive example for 'desc_to_pick_tag', clarifies the format and constraints for 'meme_text' (e.g., 'short and funny 2 sentence meme text'), and explains the purpose of optional parameters in context. This compensates well for the schema's gaps.

    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 a meme using an existing template.' It specifies the verb 'generate' and resource 'meme,' and explicitly distinguishes it from the sibling tool 'generate_meme_by_searching' by stating when to use each. This provides specific differentiation and avoids redundancy with the tool name.

    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?

    The description provides explicit usage guidelines: 'ALWAYS PREFER USING AN EXISTING TEMPLATE UNLESS THE USER REQUIRES TO SEARCH. IN THE CASE OF SEARCHING, USE: `generate_meme_by_searching()`.' It clearly defines when to use this tool versus the alternative, including a specific condition ('unless the user requires to search'), making it highly actionable 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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