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andras-tkcs

noteker

by andras-tkcs
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v0.3.4

  • Disambiguation5/5

    Only two tools with clearly distinct purposes: one processes PDFs into Markdown, the other returns server status. No overlap whatsoever.

    Naming Consistency5/5

    Both tools use the same prefix 'noteker_' and follow a verb_noun pattern ('process_pdf', 'status'), making naming predictable and consistent.

    Tool Count4/5

    With only two tools, the server is minimal but well-scoped for its stated purpose of converting handwritten notes from PDFs. Slightly low but reasonable.

    Completeness4/5

    The core functionality (PDF conversion) is fully covered, and a status tool is provided. Minor gaps like batch processing or format options are absent but not essential.

  • Average 4.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 10 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

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    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

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Annotations declare readOnlyHint=true, consistent with a read operation. The description adds detail on what is returned (version and configuration status), enhancing transparency beyond the annotation.

    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?

    Single, front-loaded sentence with no waste. Every word is necessary.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given zero parameters, an output schema, and clear read-only annotations, the description is fully adequate for selecting and invoking this 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?

    No parameters exist, so baseline is 4. The description adds nothing about parameters, but none are needed.

    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 returns 'Noteker version and configuration status,' a specific verb-resource combination. It distinguishes from the sibling 'noteker_process_pdf' which processes PDFs, not status.

    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 on when to use this tool vs others. The description implies usage for status checks but lacks explicit context or alternatives.

    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?

    The description discloses key behavioral traits: batching (max 5 pages per batch), sequential processing across batches, single combined Markdown output, and the optional 'note_context' parameter to aid resolution. The 'readOnlyHint' annotation is not contradicted as the tool does not modify input files. Slightly more detail on the max_pages cap could improve, but it's well-covered.

    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, using bullet points for parameters and separate sentences for behavior. It delivers all necessary information without fluff, earning its space with every sentence.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity (4 parameters, batching logic, output schema exists), the description covers all input aspects and processing behavior clearly. The presence of an output schema reduces the need to describe return values, and the description mentions the final result is a single combined Markdown document, making it complete for an agent to invoke correctly.

    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?

    Despite 0% schema description coverage, the description provides thorough explanations for all four parameters: file_path (absolute path), note_context (hint for ambiguous words), page_start (1-based inclusive, default 1), and page_end (inclusive, default last page with cap). It also explains batching behavior, adding significant meaning beyond the 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 tool name 'noteker_process_pdf' and description clearly state its function: converting local PDFs of handwritten notes into Markdown using Claude Vision. The verb 'Convert' and resource 'local PDF' are specific, and it implicitly distinguishes itself from the sibling 'noteker_status' which presumably checks status.

    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 explains when to use it (for converting handwritten note PDFs) and provides contextual details like batching behavior and page range handling. It does not explicitly state when not to use it, but the sibling tool 'noteker_status' suggests a clear divide, making usage context adequate.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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