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stephaneberle9

vegalite-viewer

by stephaneberle9
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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: upload_data registers datasets, while visualize_data renders them. The descriptions explicitly define when to use each, leaving no ambiguity.

    Naming Consistency5/5

    Both tools follow the same verb_noun pattern (upload_data, visualize_data), which is consistent and predictable.

    Tool Count4/5

    At 2 tools, the server is slightly under the typical 3-15 range, but the tools form a complete two-step workflow for the narrow purpose of Vega-Lite visualization, so the count is reasonable.

    Completeness4/5

    The upload-then-visualize flow covers the core workflow, but there are no management operations (e.g., listing or deleting datasets), which are minor gaps that agents can work around by re-uploading with a new name.

  • Average 4.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • 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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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. It discloses that the tool registers data for later use, implying persistence, but does not mention whether re-uploading with the same name overwrites, errors, or other side effects. Basic behavioral transparency exists, but lacks depth for a mutation tool.

    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 well-structured with clear sections for when and how to use. It is concise, with every sentence providing necessary guidance. No filler or redundant repetition of the schema.

    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?

    Given the tool's simplicity, the description covers the essential context: sequencing relative to visualize_data, data format guidelines, and the purpose. An output schema exists, so return values need not be described. Missing edge cases like name collisions or overwrite behavior, but for the core use case 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?

    Schema coverage is 0%, so the description compensates by explaining that 'name' should be a short, descriptive label and 'data' should be a list of JSON objects with consistent keys. This adds meaningful semantics beyond the bare schema types, though it stops short of giving full examples or constraints.

    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 clearly states the tool's function: 'upload and register a JSON dataset by name for use in subsequent visualizations.' It uses specific verbs ('upload', 'register') and identifies the resource (JSON dataset) and its purpose, effectively distinguishing it from the sibling tool visualize_data.

    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?

    Explicit usage instructions are given: 'When the user asks to visualize data, call this tool first to register the dataset, then call visualize_data.' It also provides how-to guidance on providing a name and data format, leaving no ambiguity about when and how to use the tool.

    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?

    With no annotations, the description carries the full burden of behavioral disclosure. It reveals that the 'data' key is injected automatically, requires a valid Vega-Lite spec, and mandates a prior upload step. However, it does not explicitly state that the tool is non-destructive or what happens with invalid specs, but the provided details are sufficient for safe use.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

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

    The description is well-structured with clear 'when to use' and numbered steps. It is longer than a typical one-liner, but every sentence provides necessary guidance for constructing valid Vega-Lite specs, and the structure aids comprehension.

    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?

    Given the tool's complexity and the presence of an output schema, the description covers prerequisites, usage steps, and critical constraints (e.g., doc link, schema validation, data injection). It gives an agent enough context to invoke the tool correctly, though it could mention error handling or result format.

    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?

    The schema has no descriptions for the parameters (0% coverage), but the description thoroughly explains both: 'name' refers to a registered dataset, and 'spec' is a Vega-Lite schema instance with specific constraints (e.g., no 'data' key). This fully compensates for the missing schema info.

    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 clearly states the tool's purpose: 'render a Vega-Lite visualization of a registered dataset directly in the chat.' It uses a specific verb ('render') and resource ('Vega-Lite visualization'), and distinguishes it from the sibling tool 'upload_data' by focusing on visualization rather than data upload.

    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 explicitly provides when-to-use guidance ('When the dataset is complex or multi-dimensional...') and when-not-to-use ('Not useful for single data points.'). It also gives a clear prerequisite workflow: upload the dataset first using 'upload_data' and then reference it by name.

    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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