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xiaobenyang-com

Prediction-Examples-in-the-Alink-Tutorial

by xiaobenyang-com
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TypeScript

Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one analyzes sentiment of text reviews, while the other predicts GMV based on year input. There is no overlap in functionality or potential for confusion between these unrelated domains.

    Naming Consistency2/5

    The naming is inconsistent with 'calc' using a generic abbreviation and 'pred_gmv' using a more descriptive verb_noun pattern. While both are short, they follow different conventions without a clear unified naming strategy.

    Tool Count2/5

    With only two tools, this server feels extremely thin for a tutorial prediction examples server. The tools cover completely unrelated domains (sentiment analysis and GMV prediction), suggesting either an incomplete implementation or a poorly scoped server that should likely be split into separate specialized servers.

    Completeness2/5

    For a tutorial examples server, there are significant gaps - each tool represents a single isolated example without supporting operations. There's no way to train models, evaluate predictions, or handle related tasks. The server feels like a collection of disconnected demonstration snippets rather than a coherent tool surface.

  • Average 2.4/5 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 Apache 2.0.

  • 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

  • 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 mentions sentiment analysis but doesn't disclose behavioral traits like confidence levels, language support, processing time, rate limits, or error conditions. The description is insufficient for a tool performing analysis without structured behavioral information.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is relatively concise but includes irrelevant information ('Alink教程第23章4节') that doesn't help understand the tool. The core purpose is stated but could be more front-loaded and focused. The structure is adequate but not optimal.

    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?

    For a sentiment analysis tool with no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't explain what the output looks like (sentiment scores, categories, confidence), doesn't mention limitations, and provides minimal context about the analysis methodology.

    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?

    Schema description coverage is 0% with 1 parameter, so the description must compensate. It mentions '输入一段评论' (input a review) which aligns with the 'review' parameter, but provides no additional semantic context about format expectations, length constraints, language requirements, or what constitutes valid input.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states the tool analyzes sentiment of a review, which is a clear purpose, but it's somewhat vague about the specific mechanism ('情感预测' - sentiment prediction) and includes irrelevant reference to 'Alink教程第23章4节' which doesn't clarify the tool's function. It doesn't distinguish from sibling 'pred_gmv' tool.

    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?

    The description provides no guidance on when to use this tool versus alternatives. It mentions analyzing sentiment of reviews but gives no context about appropriate use cases, limitations, or comparison to the sibling 'pred_gmv' tool.

    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?

    No annotations are provided, so the description carries the full burden. It states the tool performs prediction, implying it's a read-only operation that doesn't modify data, but it doesn't disclose behavioral traits such as accuracy, assumptions, data sources, error handling, or output format. The description is minimal and lacks essential context for safe and effective use.

    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 very concise with two short sentences, front-loading the purpose ('Alink教程第1章5.4节的Pipeline预测') and usage ('输入年份，预测GMV'). There's no wasted text, but it might be overly terse, lacking necessary details. Every sentence contributes, but more could be added for clarity.

    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 tool has no annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't explain what GMV is, the prediction model's basis, return values, or error cases. For a prediction tool with one parameter, this leaves significant gaps in understanding how to use it effectively.

    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 schema has 1 parameter with 0% description coverage, so the description must compensate. It mentions '输入年份' (input year), implying the parameter 'x' represents a year, but it doesn't specify format (e.g., integer year like 2023), range, or units. This adds some meaning beyond the bare schema but is insufficient for clear usage.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states the tool performs 'Pipeline预测' (pipeline prediction) for GMV given a year input, which provides a general purpose. However, it's vague about what 'GMV' specifically refers to (e.g., Gross Merchandise Value) and lacks differentiation from the sibling tool 'calc', which might also perform calculations. It's not tautological but lacks specificity.

    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?

    The description mentions '输入年份，预测GMV' (input year, predict GMV), implying usage for year-based GMV predictions. However, it provides no explicit guidance on when to use this tool versus the sibling 'calc' tool, no prerequisites, and no alternatives or exclusions. Usage is implied but not clearly defined.

    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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