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asdfas988

markfetch-mcp

by asdfas988
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a distinct output format and purpose: one returns clean markdown content, the other a PNG screenshot. While both accept a URL, there is no ambiguity about which to use based on the desired result.

    Naming Consistency5/5

    Both tools follow the verb_noun pattern (scrape_url, screenshot_url), making the naming scheme predictable and consistent. No mixed conventions or vague verbs.

    Tool Count3/5

    With only two tools, the server feels minimal but not unreasonable for its focused purpose. It is at the lower end of the typical range, so it is borderline but still acceptable.

    Completeness4/5

    The server covers the two primary ways to consume a web page—text content and visual capture. Minor potential additions like raw HTML or metadata extraction are absent, but these are not core to the stated markdown-focused purpose.

  • Average 3.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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 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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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, the description must disclose behavioral traits, but it only mentions the output format (PNG). It does not cover rendering behavior, page load handling, viewport settings, rate limits, or any side effects, leaving a significant transparency gap.

    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 sentence with no redundant words. It is concise, front-loaded, and every word earns its place.

    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 description is minimally viable for a simple one-parameter tool, covering purpose and output format. However, it lacks behavioral context such as usage alternatives, rendering specifics, and potential limitations, making it less complete than it could be.

    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 input schema fully documents the 'url' parameter with 100% coverage. The description adds no extra meaning beyond what the schema already states, so the baseline score of 3 applies.

    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 captures a screenshot of a web page as a PNG image. The verb 'Capture' and resource 'screenshot of a web page' are specific, and the mention of PNG distinguishes it from the sibling tool 'scrape_url'.

    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 is provided on when to use this tool over alternatives like 'scrape_url'. There are no exclusions, prerequisites, or contextual hints about when it is appropriate.

    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 provided, the description carries the burden of disclosing behavior. It does mention that the tool returns 'main content' and 'clean, LLM-ready markdown,' which gives insight into its extraction and formatting behavior. However, it does not disclose potential limitations (e.g., JavaScript-rendered pages, access restrictions, or handling of non-HTML content), leaving some behavioral traits unspecified.

    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 extremely concise, containing two short sentences that front-load the primary function and output format. There is no redundant phrasing or unnecessary detail; every word contributes to understanding.

    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 low complexity (one required parameter, no output schema, no annotations), the description is nearly complete. It covers what the tool does, what it returns, and when to use it. The only gap is lack of explicit differentiation from the sibling screenshot_url, but for a simple, single-purpose tool, the description provides sufficient context.

    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 input schema already provides a complete description for the single parameter 'url' ('The web page URL to read'), achieving 100% coverage. The tool description adds no additional semantics for this parameter, so the baseline score of 3 is warranted.

    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 uses specific language: 'Fetch a web page and return its main content as clean, LLM-ready markdown.' This clearly states the verb (fetch), resource (web page), and output (main content as markdown), distinguishing it from the sibling screenshot_url which captures visual images rather than textual content.

    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 'Use this to give the model the content of a URL' provides explicit usage context, indicating when the tool should be invoked. However, it does not mention alternatives or exclusions (e.g., 'for visual layouts use screenshot_url'), so it lacks full contrast. Since usage is explicitly called out, a 4 is appropriate.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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