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didacusdev

MCP Servers (OnePiece & Geolocalizar)

by didacusdev
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

67%
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 between tools. The single tool has a clear, distinct purpose focused on searching One Piece character information.

    Naming Consistency5/5

    With only one tool, naming consistency is inherently perfect. The tool name 'one_piece' follows a clear snake_case pattern and directly reflects its domain, though there is no pattern to evaluate across multiple tools.

    Tool Count2/5

    The server has only one tool, which is too few for its apparent scope covering both One Piece and Geolocalizar domains. This minimal toolset severely limits functionality and suggests an incomplete implementation for the server's stated purpose.

    Completeness1/5

    The tool surface is severely incomplete. The server name suggests coverage of both One Piece and Geolocalizar domains, but only provides a single tool for One Piece character search, with no tools for Geolocalizar and no CRUD/lifecycle operations for either domain.

  • 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 status not available

  • 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.

  • Add related servers to improve discoverability.

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

  • 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. The description only states what the tool does (search for character information) but doesn't reveal any behavioral traits such as whether it's read-only or mutative, what format the information returns in, error conditions, rate limits, or authentication requirements. This leaves significant gaps in understanding how the tool behaves.

    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 a single, clear sentence in Spanish that directly states the tool's purpose without any unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent to quickly understand the core functionality.

    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 that there are no annotations and no output schema, the description is insufficiently complete. It doesn't explain what kind of information is returned (e.g., character details, biography, abilities), the format of the response, or any error handling. For a search tool with no structured output documentation, the description should provide more context about the results.

    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 input schema has 100% description coverage (the 'id' parameter is documented as 'Id del personaje'), so the baseline score is 3. The description doesn't add any additional meaning about the parameter beyond what the schema already provides—it doesn't explain what constitutes a valid character ID, provide examples, or clarify the search scope.

    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: 'buscar informacion sobre personajes de One Piece' (search for information about One Piece characters). It specifies both the verb (search) and resource (One Piece characters). However, since there are no sibling tools mentioned, we cannot assess differentiation from alternatives, so it cannot achieve a perfect 5.

    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. It doesn't mention any prerequisites, constraints, or contextual factors that would help an agent decide when this tool is appropriate. The absence of sibling tools means there's no explicit comparison, but the description still lacks basic usage context.

    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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  • 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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