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NG-PR0JECT

Scrapiq MCP Server

by NG-PR0JECT
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion or overlap. The tool's purpose is clearly described.

    Naming Consistency5/5

    With a single tool, there is no pattern to contradict. The name 'scrapiq_extract' follows a predictable brand + verb format.

    Tool Count3/5

    A single tool feels thin for a server branded as 'Scrapiq'. While it can perform the core extraction task, the count is borderline and could benefit from additional operations like batch extraction.

    Completeness4/5

    The one tool adequately covers the primary function of extracting and cleaning web content. Minor gaps exist, such as no support for batch URLs or custom extraction rules, but there are no dead ends for the stated purpose.

  • 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
    • 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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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

  • Behavior4/5

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

    With no annotations provided, the description carries the full disclosure burden. It goes beyond a bare verb by naming what gets stripped (boilerplate, navigation, ads, scripts) and what gets returned (title, content, links, metadata). It doesn't mention potential JS-rendering or rate-limit issues, but the core behavior is well disclosed.

    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?

    Two dense sentences front-load the purpose, then give concrete behavioral details about stripping and output. Every sentence earns its place with no filler or repetition.

    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?

    The description names the exact return fields, making up for the lack of an output schema. With only three simple parameters and no nested objects, this is sufficient for an agent to call the tool correctly. Minor edge cases like network failures or API authentication are not covered, but those are not essential here.

    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 description coverage is 100%, so all three parameters (url, format, max_chars) are already documented. The description adds context about the overall curation behavior but not new parameter-level meaning, so the 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 uses a specific verb ('Extract') and resource ('a web page'), and clearly states the purpose: producing clean, structured content for LLM/RAG pipelines. Even with no siblings to distinguish from, the transformation intent is unambiguous.

    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 establishes a clear use context by targeting LLM/RAG pipelines, signaling where this extraction tool fits. There are no sibling tools to contrast with, so the lack of explicit exclusions is acceptable.

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

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