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Glama
AminHA1248

scanner-mcp

by AminHA1248
OverviewSchemaRelated ServersScoreDiscussions
Python
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Server Quality Checklist

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

  • Latest release: v0.1.1

  • Disambiguation5/5

    The two tools have completely distinct purposes: one discovers available scanners, the other performs a scan. No overlap or ambiguity exists.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern with underscores (list_scanners, scan_document), making them predictable and clear.

    Tool Count4/5

    With only 2 tools, the set is minimal but adequate for the focused domain of document scanning. Slightly under the typical 3-15 range, but still reasonable.

    Completeness4/5

    The tools cover the core workflow of discovering and scanning documents with many configuration options. Minor gaps like canceling a scan or checking scanner status are present but not critical.

  • Average 4.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 9 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 MIT License.

  • 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?

    Annotations already declare readOnlyHint=true. The description adds behavioral details: discovery methods, platform-specific drivers, and return fields. No destructive behavior is indicated, and there is no contradiction with annotations.

    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 two paragraphs, front-loaded with the main purpose. It could be slightly more concise, but it is well-structured and provides necessary technical details without redundancy.

    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 and an output schema (indicated present), the description covers the tool's purpose, discovery methods, and return format. It references the sibling tool 'scan_document', making the context 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?

    The tool has zero parameters, so baseline 4 applies. Schema coverage is 100%, and the description does not need to add parameter details.

    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 lists all reachable scanners, specifies discovery methods (mDNS, USB), and describes the return format. It distinguishes from sibling 'scan_document' by mentioning the returned 'id' field for use with that tool.

    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 implicitly indicates when to use: before scanning with 'scan_document'. It does not explicitly state when not to use, but the context is sufficient given the sibling 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?

    Annotations indicate readOnlyHint=false and destructiveHint=false, consistent with scanning. The description adds behavioral details such as creating files, returning inline images, and OCR, without contradicting annotations.

    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 a clear intro followed by bullet-pointed parameter details. It is somewhat lengthy (8 parameters) but front-loaded with the main purpose. Every sentence adds value, though could be slightly more concise if parameter details were in schema.

    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 8 parameters (none required), no output schema, the description covers all parameter details, default behaviors, return values (summary, inline images, OCR text), and error conditions (missing scanner). This is sufficient for correct tool invocation.

    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 thoroughly explains all 8 parameters including defaults, options, and effects (e.g., source options, resolution values, color modes, output formats). This fully compensates for the lack of schema parameter descriptions.

    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 states 'Scan a document and return it so Claude can read it,' clearly specifying the action (scan), resource (document), and purpose. It distinguishes from the sibling tool 'list_scanners' by focusing on scanning rather than listing.

    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 explains when to use this tool (scanning documents) and provides guidance on the scanner_id parameter, referencing list_scanners for obtaining scanner IDs, and describing default behavior and error handling for missing scanner IDs.

    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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Glama performs regular codebase and documentation scans to:

  • 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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