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j0hanz

superFetch MCP Server

by j0hanz
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.4.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'fetch-url' has a clearly defined and distinct purpose of fetching web content and converting it to Markdown.

    Naming Consistency5/5

    The single tool name 'fetch-url' follows a clear verb_noun pattern, and with only one tool, consistency is inherently perfect. There are no other tools to compare against, so no inconsistencies can exist.

    Tool Count2/5

    A single tool is too few for a server named 'superFetch MCP Server', which implies a broader or more comprehensive fetching capability. While the tool is well-described, the server's scope feels thin and limited with only one operation, making it borderline inadequate for typical agent workflows.

    Completeness2/5

    For a web content extraction server, there are significant gaps in the tool surface. It lacks operations like listing fetched content, managing caches, handling different content types beyond HTML, or providing metadata about fetched URLs. The single tool covers only the core fetch operation, leaving agents with dead ends for related tasks.

  • Average 4.2/5 across 1 of 1 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
    • Last stable release on
    • 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

  • Behavior4/5

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

    The description adds valuable behavioral context beyond annotations: READ-ONLY constraint (though annotations already have readOnlyHint=true), no JavaScript execution, auto-transformation of Git URLs, handling of truncated content via cacheResourceUri, and error handling strategies. These details provide practical implementation guidance that annotations alone don't cover.

    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 well-structured with clear role/task/constraints sections. Every sentence earns its place by providing essential operational guidance. It's front-loaded with the core purpose and efficiently organized without any wasted words.

    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 complexity of a web fetching tool with multiple operational constraints, the description provides substantial behavioral context. However, without an output schema, it doesn't explain return values or format details. The annotations cover safety aspects well, and the description adds important implementation details, making it mostly complete.

    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?

    With 100% schema description coverage, the input schema already documents all parameters thoroughly. The description doesn't add any additional parameter semantics beyond what's in the schema, so it meets the baseline expectation without providing extra value.

    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 with specific verbs ('fetch', 'convert') and resources ('public webpages', 'HTML to clean Markdown'). The role/task structure explicitly defines it as a Web Content Extractor that fetches and converts web content, making the purpose immediately clear and distinct.

    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 constraints section provides clear guidance on when to use specific features (e.g., use task mode for large pages/timeouts, retry with task mode for queue_full errors). However, there are no explicit alternatives mentioned since there are no sibling tools, so it doesn't differentiate from other tools.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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