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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one searches for entries, the other fetches a specific entry by ID. There is no overlap or confusion between them.

    Naming Consistency4/5

    Both tools share the 'aiwiki_' prefix, which is good, but the second word differs in style: 'search' is a verb, while 'get_entry' is verb_noun. This is a minor deviation from a fully consistent verb_noun pattern.

    Tool Count3/5

    With only 2 tools, the server is at the low end of the scale. However, for a knowledge base encyclopedia, a search and a fetch tool can be a reasonable minimal set, though it feels slightly thin.

    Completeness4/5

    The server covers the core workflow of discovering and retrieving entries. A notable gap is the lack of a list/browse function to see all entries without a query, but search and retrieval are sufficient for basic use.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 103 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • This repository is licensed under CC BY-SA 4.0.

  • 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

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about the content perspective (first-person) and query language flexibility (Chinese/English), but it omits details such as how results are ordered, whether search requires exact matches, or what variations in output might occur. The description is adequate but not deeply transparent about search behavior beyond the basic action.

    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 compact and front-loaded: it states the tool's purpose, domain, concrete use cases, and language flexibility in just two sentences. The parenthetical examples are efficient and informative without padding. Every element earns its place, making this an exemplary model of concise yet informative writing.

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

    Completeness4/5

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

    Given the tool's moderate complexity (4 parameters, no annotations, no output schema), the description provides strong domain context, examples of query topics, and language support. The schema already hints at return structure via the 'returned titles/summaries/urls' phrase in the lang parameter. However, the description could have explicitly tied to the sibling 'aiwiki_get_entry' for next steps, which would have made it fully complete. It covers most contextual needs without being exhaustive.

    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 schema already provides 100% coverage for all four parameters (query, lang, limit, subject), so the baseline is 3. The description adds minimal parameter-specific meaning beyond the schema—it reiterates that queries can be Chinese or English, which duplicates the schema's query description. It does not introduce new parameter semantics or clarify parameter interactions, so the score stays at baseline.

    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 opens with a specific verb and resource ('Search AiWiki') and immediately defines the resource as 'an encyclopedia of AI-coding pitfalls and LLM privacy protection'. This clearly distinguishes the search tool from its sibling 'aiwiki_get_entry' by scope (searching vs. retrieving) and content domain, making the tool's purpose unmistakable.

    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 states when to use the tool: 'Use when the user hits a recurring AI-coding failure... or an LLM privacy question...', with concrete examples. It does not explicitly list when-not-to-use or name the alternative 'aiwiki_get_entry', but the usage context is rich enough that an agent can infer the appropriate scenario. This is a clear context, though not a full when/when-not set.

    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?

    No annotations are provided, so the description carries the burden. It discloses the output format ('full markdown') and content structure ('mechanism analysis, consequences, best practices, verifiable sources'), but does not mention error handling or language fallback (although the fallback is in the schema). This adds some transparency but not exhaustive detail.

    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 no filler. The key action and resource are front-loaded, and the second sentence explains entry content, efficiently using the space.

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

    Completeness4/5

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

    Given the tool's simplicity (id + optional lang, no output schema), the description is complete enough. It specifies what the entry contains and that the output is markdown. It does not discuss edge cases, but for a basic retrieval tool this is acceptable.

    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?

    Schema coverage is 100% for both parameters, so the baseline is 3. The description adds semantic value by clarifying the id is 'as returned by aiwiki_search', linking it to the search tool's output. This enriches the parameter meaning beyond the schema description.

    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 specifies the action ('Fetch the full markdown') and the resource ('one AiWiki entry by id'). It also differentiates from the sibling aiwiki_search by indicating the id comes from that search 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 phrase 'as returned by aiwiki_search' provides clear context that this tool is used after searching. It does not explicitly state when not to use it, but the context is sufficient for a simple fetch tool.

    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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