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TomMelt

HPC-MCP

by TomMelt

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 between tools, as there are no other tools to compare it to. The tool's purpose is clearly defined and distinct by default.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect—there are no other tools to create inconsistency or mixed conventions. The tool name follows a clear verb_noun pattern.

    Tool Count2/5

    A single tool is too few for a server named 'HPC-MCP', which suggests a High-Performance Computing domain that typically involves complex workflows like job submission, monitoring, and resource management. This minimal toolset severely limits functionality.

    Completeness1/5

    The tool surface is severely incomplete for an HPC domain; it only provides a debug function, missing essential operations such as job submission, status checking, file management, and resource allocation, which are core to HPC workflows.

  • 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.

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    {
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      "maintainers": [
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      ]
    }

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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool returns a stack trace, which is useful, but fails to describe critical traits like whether it requires specific permissions, if it's read-only or destructive, how it handles errors, or any rate limits. For a tool that interacts with a program (potentially involving execution), this is a significant gap 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.

    Conciseness5/5

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

    The description is extremely concise—a single sentence that directly states the tool's action and output without any fluff. It is front-loaded with the core purpose, making it efficient for quick understanding. Every word earns its place, adhering to best practices for brevity.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity (involving program debugging), lack of annotations, and 0% schema description coverage, the description is incomplete. It mentions the output (stack trace), and an output schema exists, which helps, but it misses critical context like behavioral traits and parameter meanings. This makes it minimally adequate but with clear gaps for effective agent use.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, so the description must compensate for the lack of parameter documentation. It does not explain what 'target' or 'args' represent (e.g., target could be a file path or process ID, args might be command-line arguments), their formats, or examples. This leaves the parameters largely ambiguous, reducing the tool's usability despite the schema defining their types.

    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 with a specific verb ('debug') and resource ('crashing program'), and specifies the output ('return the stack trace'). It distinguishes the action from generic debugging by focusing on crash analysis. However, without sibling tools, differentiation isn't tested, preventing a perfect 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 constraints. It states what the tool does but offers no context for its application, such as when debugging is appropriate or what types of crashes it handles. This lack of usage context limits its helpfulness for an 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:

  • 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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