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christianhoustonfloyd

schema-viz

by christianhoustonfloyd
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely separate concerns: one checks server status, the other renders a schema. There is no overlap or ambiguity in their purposes.

    Naming Consistency5/5

    Both tool names follow a clear verb_noun snake_case pattern: get_schema_viewer_status and display_schema. The naming is consistent and predictable.

    Tool Count3/5

    Two tools is borderline thin for a server, though each serves a clear purpose. The scope is narrow, but a stop-viewer or close-browser tool would make the set feel more complete.

    Completeness4/5

    The core workflow of rendering a schema and checking viewer status is covered. The lack of a way to close or stop the viewer is a minor gap that agents can work around by simply refreshing or leaving the browser tab.

  • Average 3.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

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  • 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.

  • Add a glama.json file to provide metadata about your server.

  • 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.

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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

  • Behavior3/5

    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 states the core behavior (returns running status and URL) but does not disclose edge-case behavior such as what is returned when the server is not running (null URL, error, or false flag). The 'running' semantics are also undefined.

    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 13-word sentence with zero waste. The key verb and resource are front-loaded, and everything stated earns its place.

    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?

    For a zero-parameter, no-output-schema status check, the description covers the essential contract: what is returned. It could optionally describe the return shape (e.g., boolean + URL field) or suggest using it before display_schema, but nothing critical is missing for such a simple tool.

    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 tool has zero parameters, so per the rubric the baseline is 4. With an empty schema, there is nothing for the description to explain about arguments, making this dimension inherently satisfied.

    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 uses a specific verb ('Return') with a clear resource (schema viewer server status) and names the output (running state + URL). It is unambiguous about what the tool does, though it does not explicitly differentiate from its sibling 'display_schema' — the distinction is implied by subject matter rather than stated.

    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?

    No guidance is provided on when to call this tool versus 'display_schema' or any other alternative. An agent must infer that a status check likely precedes schema display. There are no explicit conditions, exclusions, or workflow hints.

    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?

    No annotations are present, so the description carries the behavioral burden. It discloses the local-browser rendering, live-reload behavior, input precedence, and fallback to the bundled schema. It does not mention non-destructiveness or return behavior explicitly, but for a rendering tool the disclosed behavior is reasonably transparent.

    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 three focused sentences with the core purpose front-loaded. It avoids restating parameter descriptions and every sentence adds useful information: what it renders, how inputs are selected, and how the browser view updates.

    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?

    For a viewer tool with five parameters and a nested `context` object, the description plus rich schema descriptions cover input modes, fallback behavior, and live reload. No output schema exists, but the tool's visible output is the browser diagram, so return-value details are not critical here. Minor gaps like persistence or close behavior do not materially hurt usability.

    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%, so the baseline is 3. The description adds cross-parameter meaning beyond the schema by stating that `dbml` takes precedence over `filePath` and that supplying neither falls back to the bundled project schema. This clarifies relationships the individual parameter descriptions do not fully convey.

    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 names a specific verb ('Render'), resource ('database schema'), and output form ('interactive, dbdiagram.io-style ER diagram'), which is clearly distinct from the sibling get_schema_viewer_status. The added details about draggable cards, relationship lines, and pan/zoom make the tool's function unambiguous.

    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?

    The description gives explicit input-selection guidance: use `dbml` for raw DBML, `filePath` for a .dbml file, and neither for the bundled schema. However, it does not position this tool against get_schema_viewer_status or state any when-not-to-use conditions, so the guidance stops short of full alternative routing.

    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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  • Evaluate tool definition quality.

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