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haoxinm

pdf-chart-parser

by haoxinm
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

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 zero ambiguity. An agent cannot confuse it with any other tool in the set.

    Naming Consistency5/5

    With a single tool, naming consistency is trivially maintained. The name 'extract_usage_chart' follows a clear verb_noun pattern.

    Tool Count5/5

    A single tool is appropriate for the narrow, focused domain of extracting chart data from utility bill PDFs. Adding more tools would likely be unnecessary.

    Completeness5/5

    For its stated purpose of extracting energy-usage chart data, the tool provides text, structured JSON, and optional annotated images. There are no obvious gaps in the surface for this specific task.

  • Average 4.1/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 9 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 AGPL 3.0.

  • This repository includes a README.md file.

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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?

    Since no annotations are provided, the description must disclose behavioral traits. It explains the output structure (Markdown, JSON, optional image) and highlights a key non-determinism: the caller must determine which series corresponds to which utility. It does not mention error handling or authentication, but the core behavior is well-described.

    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 well-structured: first sentence states purpose, second lists outputs, third gives input constraint, then addresses output ambiguity. Every sentence adds value without redundancy. It is appropriately sized for the tool's complexity.

    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 has 8 parameters, no schema descriptions, no annotations, and no output schema, the description covers the essential aspects: input requirements, output structure, and an important ambiguity resolution. It falls short only by not describing all parameters (page, chart_type, value_unit, render_dpi) and not specifying error cases. Still, it provides substantial context for an AI agent.

    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?

    The input schema has 8 parameters with 0% description coverage. The description adds meaning for a few parameters: the mutual exclusivity of pdf_path, pdf_base64, and pdf_url, and the optionality of return_annotated_image. However, it does not explain page, chart_type, value_unit, or render_dpi, leaving the agent to infer their purposes from the schema enums and defaults.

    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: 'Extract energy-usage chart data from a utility-bill PDF.' It specifies the input (PDF) and outputs (Markdown, JSON, optional annotated PNG), leaving no ambiguity 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 Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides explicit guidance on input: 'Provide exactly one of pdf_path, pdf_base64, or pdf_url.' It also explains how to interpret the output series when multiple utilities are present. However, it does not mention when to avoid using this tool or any prerequisites, which would be helpful.

    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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