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ZhuoS62

cn-llm-mcp

by ZhuoS62
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one for drafting code patches and one for general low-risk tasks. No overlap in functionality.

    Naming Consistency5/5

    Both tools use a consistent verb_noun pattern in snake_case (draft_code_patch, run_cn_model), making the naming predictable.

    Tool Count3/5

    With only 2 tools, the server feels minimal but still focused. The count is borderline for the scope but not extreme.

    Completeness2/5

    The tool surface is severely incomplete for delegating tasks to a Chinese LLM; missing operations like listing models, managing tasks, or retrieving results beyond the initial call.

  • Average 3.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 4 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

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  • 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

  • Behavior3/5

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

    With no annotations, the description carries full disclosure burden. It mentions delegation to a Chinese LLM provider and the need for review, but lacks details on behavioral traits such as timeouts, error handling, or data sovereignty. The transparency is adequate but minimal for a tool delegating to an external provider.

    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 consists of two concise sentences that front-load the purpose and usage guidelines. Every sentence adds value: one for delegation and use cases, one for the review requirement. No unnecessary information.

    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?

    For a tool with 6 parameters, no output schema, and no annotations, the description is brief. It addresses the task's nature and usage but omits crucial context like output format hints, error scenarios, or cost/speed trade-offs. Adequate but leaves gaps given the complexity.

    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?

    Schema description coverage is 67%, with 4 of 6 parameters explained in the schema itself. The tool description adds no new parameter insights beyond the schema. Given high coverage, baseline is 3; the description does not compensate for uncovered parameters (max_tokens, temperature).

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

    Purpose4/5

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

    The description clearly states the tool delegates tasks to a Chinese LLM provider and lists specific use cases (drafts, summaries, simple code generation, mechanical edits). While it distinguishes from the sibling 'draft_code_patch' by emphasizing simplicity and delegation, the differentiation could be more explicit, hence a 4 rather than 5.

    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 clear guidance on when to use the tool ('Best for drafts, summaries, simple code generation, and mechanical edits') and explicitly requires review of results. However, it does not specify when not to use it or mention alternative tools, so it falls short of a 5.

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

  • Behavior3/5

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

    With no annotations, the description must cover behavioral traits. It mentions the tool drafts a diff (not applying it) and involves a Chinese LLM provider. However, it omits details like authentication, rate limits, whether it modifies state, or what happens on error. The caution to inspect before applying is useful but not exhaustive.

    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?

    Two sentences with no redundancy. The action, scope, and caution are front-loaded. Every word adds value.

    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?

    The tool has 5 parameters and no output schema. The description explains the purpose and usage constraints but fails to describe the return value (the diff). Given complexity, it covers most aspects but the missing output specification lowers completeness.

    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?

    Schema coverage is 60%. The description adds value for 'task' and 'files' (e.g., 'Keep this list small'), but 'max_tokens' and 'temperature' lack descriptions in both schema and description. The description does increase clarity for half the parameters, but the gap for the other two prevents a higher score.

    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 action ('Ask a Chinese LLM provider to draft a minimal unified diff') and the resource ('small code task'). It distinguishes from the sibling tool 'run_cn_model' by specifying a focused use case (code patch drafting) rather than general model execution.

    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 explicitly advises using this tool 'only for low-risk changes' and instructs to 'inspect and test the patch before applying it.' It provides clear context but does not enumerate alternatives or explicitly state when not to use it beyond the risk qualifier.

    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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