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maqi1520

md2card-mcp-server

by maqi1520
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.10

  • Disambiguation5/5

    The two tools have clear, non-overlapping purposes: md2card_api converts Markdown to knowledge cards, while wechat_scraper fetches WeChat articles and converts them to Markdown. An agent can easily distinguish when to use each.

    Naming Consistency2/5

    The naming pattern is inconsistent: 'md2card_api' uses an acronym-plus-suffix style, while 'wechat_scraper' uses a plain noun phrase. Neither follows a verb_noun convention, and the two names have no shared structural pattern.

    Tool Count3/5

    With only two tools, the server feels minimal. However, the niche scope (Markdown-to-card conversion with a WeChat import pathway) arguably justifies a small surface, but it is still on the borderline of being too thin.

    Completeness4/5

    The core workflow is covered: scrape WeChat content to Markdown, then convert Markdown to cards. Minor gaps exist (e.g., no direct URL-to-card conversion or card customization options), but the primary pipeline is functional.

  • Average 3.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 is failing

  • 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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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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?

    Annotations are absent, so the description must disclose behavioral traits. It only provides a high-level description without mentioning any side effects, output format, or operational details such as file handling or error behavior.

    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 a single, front-loaded sentence that conveys the core function without unnecessary words, earning maximum conciseness.

    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?

    The tool has 10 parameters, no output schema, and no annotations. A one-sentence description does not adequately cover usage context, expected output, or edge cases, leaving significant gaps for the agent.

    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 100%, so baseline is 3. The description adds no parameter-specific information beyond what the schema already documents, so no additional value is provided.

    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 a specific verb (convert) and resource (Markdown documents to knowledge cards). It lacks explicit differentiation from sibling tools, but the sibling is unrelated, so the purpose is nonetheless clear.

    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 on when to use this tool versus alternatives. It doesn't mention any scenarios or exclusions, leaving the agent to infer usage from the name and schema.

    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 must fully disclose behavioral traits. It only states the core fetch-and-convert action without details on error handling, network requirements, anti-scraping issues, or the exact output format. This is a significant gap for a scraper tool.

    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 one concise sentence that is front-loaded and contains no redundant information. Every word contributes meaning, making it an efficient and well-structured statement.

    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?

    The tool is a scraper with one parameter and no output schema, yet the description does not explicitly explain what the return value is (e.g., whether Markdown is returned as a string, file, or in another format). It also lacks information about error behavior or limitations, leaving the agent under-informed for a non-trivial operation.

    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 fully documents the single 'url' parameter with a clear description, achieving 100% schema coverage. The tool description adds no additional context beyond what the schema provides, so a baseline score of 3 is appropriate.

    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's function: fetching WeChat official account articles and converting them to Markdown. The verb '抓取' specifies the action and resource explicitly, and it distinguishes itself from the sibling tool md2card_api, which likely performs the reverse operation.

    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, nor does it mention prerequisites or edge cases. While the sibling tool exists, no differentiation is offered, leaving the agent without context for selecting this tool over others.

    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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