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kimdonghwi94

MCP WebAnalyzer

by kimdonghwi94
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one converts web pages to markdown for general content extraction, while the other answers specific questions about web page content using RAG. There is no overlap in functionality or ambiguity about when to use each tool.

    Naming Consistency5/5

    Both tools follow a consistent snake_case naming pattern with descriptive names that clearly indicate their function: url_to_markdown_tool and web_content_qna. The naming convention is uniform and predictable across the tool set.

    Tool Count2/5

    With only 2 tools, the server feels thin for a web analysis domain. While the tools cover basic extraction and Q&A, there are likely missing operations like content summarization, metadata extraction, or batch processing that would make the set more complete and useful for agents.

    Completeness2/5

    For a web analysis server, the tool surface has significant gaps. It lacks essential operations such as summarizing content, extracting structured data (e.g., tables, links), analyzing page structure, or handling multiple URLs. Agents will struggle with common web analysis tasks beyond the two provided tools.

  • Average 4.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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.

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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?

    No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it performs web scraping and uses AI (RAG) to generate answers, which implies external API calls and potential rate limits or latency. However, it lacks details on error handling, authentication needs, or specific limitations (e.g., website compatibility, content size). The description does not contradict any annotations.

    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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by brief elaboration on the method, and ends with clear Arg/Return sections. Every sentence adds value without redundancy, and the structure is logical and efficient.

    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 complexity (involving web scraping and AI), no annotations, and an output schema exists (returns a string), the description is mostly complete. It covers purpose, method, parameters, and return type, but could benefit from more behavioral context (e.g., limitations, errors). The output schema reduces the need to explain return values, but the description still lacks some operational details.

    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 description coverage is 0%, so the description must compensate. It adds meaningful semantics beyond the schema by explaining that 'url' is for 'the web page URL to analyze' and 'question' is 'the question to answer based on the page content', clarifying their roles. However, it does not provide format examples or constraints (e.g., URL validation, question phrasing). With 0% coverage and 2 parameters, this is above 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 clearly states the tool's purpose as 'Answer questions about web page content using RAG' with specific verbs ('answer questions', 'combines web scraping with RAG', 'extracts relevant content sections') and distinguishes it from the sibling tool 'url_to_markdown_tool' by focusing on Q&A rather than conversion. It explicitly mentions the resource (web page content) and method (RAG).

    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 context for when to use this tool ('to answer specific questions about web page content'), but does not explicitly state when not to use it or name alternatives. It implies usage for Q&A tasks involving web content, though lacks explicit exclusions or comparisons to the sibling tool beyond their different functions.

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

  • Behavior4/5

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

    With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it scrapes web pages, removes unnecessary elements, ranks content by importance using a custom algorithm, and returns clean markdown. This covers the transformation process and output characteristics beyond basic functionality.

    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 efficiently structured with a clear purpose statement, elaboration of the process, usage context, and separate Args/Returns sections. Every sentence adds value without redundancy, and the information is appropriately front-loaded.

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

    Completeness5/5

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

    Given the tool's moderate complexity (web scraping with algorithmic ranking), no annotations, and the presence of an output schema (which handles return value documentation), the description provides complete context. It explains the transformation process, use case, parameter meaning, and output format adequately.

    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?

    With 0% schema description coverage and only one parameter, the description compensates well by explaining the 'url' parameter as 'The web page URL to analyze and convert,' adding meaningful context about its purpose. However, it doesn't specify URL format requirements or constraints, preventing a perfect 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 specific action ('Extract and convert web page content to markdown format') and distinguishes it from the sibling tool 'web_content_qna' by focusing on conversion rather than Q&A. It provides a complete verb+resource+output specification.

    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 'Perfect for RAG applications,' providing clear context for when to use this tool. However, it doesn't specify when NOT to use it or explicitly contrast with the sibling 'web_content_qna' tool, which would be needed for a score of 5.

    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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