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neocortex

QuickChart MCP Server

by neocortex
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap. The tool's purpose is singular and clear.

    Naming Consistency5/5

    The single tool name 'generate_chart' follows a clear verb_noun pattern, and with only one tool, consistency is inherently maintained.

    Tool Count3/5

    The server has only one tool, which is borderline for typical expectations. However, for the narrow domain of chart generation, it may be sufficient, though it feels thin.

    Completeness5/5

    The tool covers both URL generation and file download, with configurable output paths, fully addressing the core chart generation workflow without obvious gaps.

  • Average 4.3/5 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

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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.

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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

  • 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 clearly explains the two output modes (URL vs. file path), the default output_path behavior, and the return value. It also implicitly discloses file-writing side effects when download=True, which is valuable context.

    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 Args and Returns sections, and every sentence provides essential information. It is concise with no redundancy, making it easy for an AI agent to parse and act upon.

    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 description covers the core aspects: inputs, download options, and return values. It does not mention potential network dependencies or error conditions, but for a tool of this complexity, the provided context is largely adequate. The presence of a detailed input schema further fills in the gaps.

    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's top-level parameters have 0% description coverage, so the description must compensate. It fully does: chart_input is summarized as 'configuration including type, datasets, labels, title, and options', download explains its boolean effect, and output_path clarifies its optional nature and default destination. This adds significant meaning beyond the raw 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 clearly states 'Generate a chart using QuickChart', which is a specific verb+resource combination. It unambiguously identifies the tool's purpose and the service it wraps, leaving no room for confusion about what the tool does.

    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 does not discuss when to use this tool versus alternatives, and no sibling tools are provided for context. It offers indirect usage guidance through parameter descriptions (e.g., download=True to save), but there is no explicit statement of when this tool should be chosen.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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