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MortalPastry

wyrd-mcp

by MortalPastry

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.0

  • Disambiguation5/5

    Only a single tool exists, so there is no possibility of confusing it with another. The tool's purpose is singular and precisely described.

    Naming Consistency5/5

    The lone tool name 'read' is a clear, conventional verb that directly matches its action. There are no conflicting naming styles to evaluate.

    Tool Count3/5

    One tool is at the thin end of the range, but it serves a narrow, well-defined purpose (bounded reads from a single folder). The server is not bloated, yet it feels minimal.

    Completeness3/5

    The read operation itself is thoroughly implemented with offset pagination and encoding safeguards, but there is no way to list or stat files in the folder, forcing agents to know exact paths in advance.

  • Average 5/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 3 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.

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    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
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      ]
    }

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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

  • Behavior5/5

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

    With no annotations, the description carries the full behavioral burden and does so thoroughly. It discloses byte-oriented reads, possible truncation with `next_offset`, refusal rules, the `NOT_TEXT` error for invalid UTF-8, and even known security limitations like hard links and reparse points. This is exemplary transparency beyond what the schema or annotations provide.

    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 long, but every paragraph earns its place by adding operationally relevant detail. It is front-loaded with the core purpose, followed by path rules, byte semantics, scope, encoding behavior, and known limits, with clear section headers that make it easy for an agent to scan.

    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?

    Despite having no output schema, the description explains what the response will contain (`truncated: true`, `next_offset`, `NOT_TEXT` refusals) and covers all caller-relevant behavior. An agent has everything needed to use the tool correctly: path constraints, offset/limit semantics, continuation mechanism, edge cases, and security caveats.

    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?

    Although the schema covers all three parameters, the description adds meaning beyond the schema by explaining that `offset` and `limit` are byte counts into UTF-8 encoding, that a slice may end up to 3 bytes shorter to preserve codepoints, and that callers should use `next_offset` from a truncated response. It also clarifies path semantics with concrete examples and refusal cases.

    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 first sentence states a specific action and resource: 'Read a byte-bounded slice of one file from the single folder this server was granted.' It precisely defines scope and behavior, making it easy for an agent to know exactly what the tool does even without siblings to distinguish it from.

    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 gives explicit when-to-use and when-not-to-use guidance: every file inside the granted folder is in scope, absolute paths and `..` traversal are refused, and non-UTF-8 content is refused. The 'WHAT IS IN SCOPE' and 'KNOWN LIMITS' sections provide clear boundary conditions, which fully compensates for the absence of sibling tools.

    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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