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memebo-at

Memeboat MCP

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by memebo-at
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Server Quality Checklist

83%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.2.1

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: searching templates, fetching template details, and creating memes. There is no overlap or ambiguity.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern (search_meme_templates, get_meme_template, create_meme), using underscores and a clear action-object structure.

    Tool Count4/5

    With only 3 tools, it is slightly minimal but still covers the essential operations for meme creation. The scope is narrow and appropriate for the service.

    Completeness5/5

    The tool set covers the full workflow: discover templates via search, examine a template, and create a meme. There are no obvious missing operations for the stated purpose.

  • Average 4.4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 13 commits in the last 12 weeks
    • Last stable release on
    • 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.

  • This repository includes a glama.json configuration file.

  • 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.

  • View related servers

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

  • Behavior3/5

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

    Describes return fields (slug, image URL, caption count) but lacks detail on pagination, ordering, empty results handling, or rate limits. No annotations are provided to supplement.

    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?

    Two sentences efficiently convey purpose, examples, and return values. No extraneous information.

    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?

    Comprehensive for a search tool with two parameters. Explains return fields, but could benefit from mentioning result ordering or pagination limits.

    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 coverage is 100%, and the description adds value with query examples ('drake', 'distracted boyfriend') and clarifies the purpose of each parameter beyond the 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 it searches meme templates by name or topic with specific examples. It distinguishes itself from siblings by focusing on search vs. retrieval or creation.

    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?

    Provides example queries but no explicit guidance on when to use this tool versus get_meme_template or create_meme. Does not mention when not to use it.

    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?

    With no annotations provided, the description carries full burden. It discloses that the tool returns dimensions, image URL, and suggested caption count. It does not mention side effects, errors, or rate limits, but for a read-only operation the description is adequate and adds value beyond the input schema.

    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?

    A single, well-structured sentence that front-loads the action and result. Every word adds value; no filler or repetition.

    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 simplicity (one param, no output schema, no annotations), the description covers purpose, input source, and output contents. It lacks mention of output format (e.g., JSON), but for a straightforward fetch, the description is largely complete.

    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 100% with the slug parameter described. The tool description adds context on how to obtain the slug (from search or URL), which is helpful beyond the schema's example. Baseline of 3 is exceeded with this extra guidance.

    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 'Fetch one meme template by its slug' with the specific verb 'Fetch' and resource 'meme template'. It differentiates from siblings: search_meme_templates returns multiple, create_meme creates. The return contents (dimensions, image URL, caption count) are explicitly listed.

    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 specifies the source of the slug ('from search_meme_templates or a memebo.at/templates/<slug> URL'), providing clear context on how to obtain the input. It does not explicitly state when not to use, but the simple fetch operation makes alternative guidance less critical.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

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

    With no annotations, the description fully discloses behavior: caption order logic, optional styling, layout options, and return values (meme page URL and direct image URL). Also notes rate limiting per IP.

    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?

    Single paragraph with no redundancy. Front-loaded with purpose, each sentence adds essential information. 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?

    For a 5-parameter tool with no output schema, the description adequately explains parameter behavior, return values, and constraints (rate limiting). No gaps identified.

    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?

    Schema coverage is 100%, but description adds significant value beyond schema by explaining caption ordering rules and layout behavior ('top' puts captions in a white bar). Provides context not in 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 verb ('create'), resource ('real, shareable meme'), and platform ('memebo.at'). It distinguishes from siblings by focusing on creation rather than retrieval or search.

    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?

    Describes caption placement rules and mentions rate limiting ('space out repeated calls'), but does not explicitly state when to use this tool versus siblings like get_meme_template or search_meme_templates.

    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:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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