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vktr93

Reviewmetric

by vktr93
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Python
Remote

Server Quality Checklist

83%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.1

  • Disambiguation5/5

    Only two tools with clearly distinct purposes: one is for submission, the other for checking status. No overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern in snake_case (submit_manuscript, check_analysis_status), with clear and descriptive names.

    Tool Count2/5

    With only two tools for a described 15-agent AI pipeline, the tool count feels too thin for the apparent scope, lacking intermediate or management tools.

    Completeness2/5

    The tool surface is severely limited, covering only submission and status checking. Missing are operations such as listing runs, retrieving full reports, or managing analyses.

  • Average 3.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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

  • 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

  • Behavior2/5

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

    With no annotations, the description must disclose behavioral traits. It mentions submission to a 15-agent pipeline but omits details on execution time, error handling, or side effects (e.g., file access requirements). Minimal behavioral 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 two sentences with a structured args list. It is front-loaded with the core purpose and includes only necessary details, with no wasted words.

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

    Completeness3/5

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

    Given the tool's single parameter and presence of an output schema, the description covers the basic action but lacks details on whether the submission is synchronous or asynchronous, and what the immediate response indicates. It is adequate but not fully informative.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The description adds meaning to 'file_path' by specifying it must be an absolute local path for a .docx file. Since schema description coverage is 0%, this extra info is valuable but still leaves out format 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 submits a .docx manuscript to a specific AI pipeline, using a strong verb ('submits') and specifying the resource type and destination. It distinguishes itself from the sibling 'check_analysis_status' by focusing on submission.

    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 is provided on when to use this tool versus the sibling 'check_analysis_status' or any prerequisites. The description only states what it does without context on appropriate usage.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    With no annotations provided, the description carries full burden. It only states the tool checks status or retrieves report, but fails to disclose idempotency, side effects, error handling, or whether it requires prior submission. This is minimal behavioral insight.

    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 brief and front-loaded with the main purpose. The Args section is standard but not wasteful. It could be slightly tighter by integrating the parameter information into the main sentence, but still concise.

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

    Completeness2/5

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

    Given the presence of an output schema (not shown but indicated), the description does not need to detail return values. However, it lacks context on how the tool fits into the workflow (e.g., must be called after submit_manuscript), what happens if run_id is invalid, or whether it is polling versus one-shot. The description is incomplete for a tool with no annotations.

    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 schema has one required parameter 'run_id' with only type and title. The description adds meaning by specifying it as 'the unique UUID of the analysis run', which clarifies the expected format beyond the schema. Since schema coverage is 0%, this addition is valuable.

    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 checks status or retrieves a final report of a ReviewMetric analysis. The verb ('checks', 'retrieves') and resource ('ReviewMetric analysis') are specific, and it distinguishes from the sibling tool 'submit_manuscript' which handles a different workflow step.

    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 implies the tool is used after submitting an analysis (sibling 'submit_manuscript'), but no explicit guidance on when to check status versus retrieve report, nor any exclusions or prerequisites. The usage context is somewhat clear but lacks depth.

    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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