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miguelju

vle-mcp

by miguelju
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion or overlap.

    Naming Consistency5/5

    The single tool uses a clear lowercase_with_underscores naming convention, consistent with common practices.

    Tool Count3/5

    The server has only one tool, which is minimal. While it may serve a specific narrow purpose, it lacks the typical breadth expected of a functional server.

    Completeness3/5

    The tool covers a single calculation function. For a dedicated water properties server, this might be sufficient, but it offers no related utilities or additional thermodynamic properties, leaving likely gaps.

  • Average 4.2/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 4 commits in the last 12 weeks
    • No stable releases found
    • 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.

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  "$schema": "https://glama.ai/mcp/schemas/server.json",
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  ]
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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?

    With no annotations provided, the description carries the burden of behavioral disclosure. It correctly emphasizes absolute pressure over gauge, which is a key behavioral caveat. However, it does not mention any limitations such as temperature ranges, precision, or what the output looks like beyond the implicit calculation. The output schema may cover the return format, but the description itself is somewhat sparse on behavioral details.

    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 concise and front-loaded with the main purpose. It uses a short example and a clear warning about gauge pressure, with no extraneous content. Every sentence serves a purpose, making it easy for an agent to quickly grasp what the tool does and how to use it correctly.

    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?

    The tool is simple, and the output schema likely defines the return value, so the description doesn't need to explain that. It covers the essential usage context: the pressure type and a common unit example. It could be slightly more complete by mentioning the range of pressures supported or any assumptions about water purity, but for a straightforward thermodynamic calculation, it is sufficiently 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?

    The schema itself documents both parameters with types, defaults, and an enum for pressure_unit. Since schema description coverage is 0%, the description must add meaning beyond the schema. It does by explaining the absolute pressure requirement and providing the conversion factor for atm to kPa, which helps users correctly set the pressure parameter. This is meaningful value added beyond the structured 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 states the specific verb 'Calculate' and the resource 'water's boiling/saturation temperature', and clearly specifies the input as absolute pressure. It provides a concrete example question and clarifies that standard atmosphere is 1 atm or 101.325 kPa absolute, leaving no ambiguity about the tool's function.

    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 explicitly says 'Use this for questions such as...' and gives a clear example. It also warns against using gauge pressure, which is a critical usage constraint. Although there are no sibling tools to differentiate from, the guidance is clear and actionable.

    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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