article-scraper-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Only one tool exists, so there is no possibility of confusion between tools. The purpose is singular and clear.
Naming Consistency5/5With only one tool, naming consistency is not an issue. The name 'fetch_article' follows a clear verb_noun pattern.
Tool Count3/5One tool is minimal for a server named 'article-scraper-mcp', which might imply more features like batch fetching or source listing. However, the tool is functional and well-defined, so it is borderline acceptable.
Completeness4/5The single tool covers the core operation of fetching a structured article from a URL. Minor gaps exist, such as lack of support for multiple articles or error handling variations, but the basic use case is fully addressed.
Average 4/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed 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.
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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, the description carries the full burden. It discloses that the tool raises ValueError for invalid URLs and requests.RequestException for HTTP failures. It does not mention other behaviors like caching, speed, or idempotency, but for a simple fetch tool, the error disclosure is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with clear sections for Args, Returns, and Raises. It is comprehensive without being verbose. A slight reduction in verbosity (e.g., removing trivial lines) could improve conciseness, but it remains efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool (one parameter, no siblings), the description is complete. It explains input, output format, and error conditions. An output schema exists, so the return structure is also formally defined. No gaps remain for an agent to use this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'url' has a brief description 'The URL of the news article' in the docstring, but schema coverage is 0% (the schema has no description). The description adds minimal meaning beyond the parameter name. For a single required string parameter, this is adequate but not exemplary.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a news article by URL and returns structured data with specific keys (title, text, author, date). The verb 'Fetch' and resource 'news article' are specific, and the return structure is explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No sibling tools are provided, so differentiation is not required. However, the description lacks explicit guidance on when to use this tool versus alternatives (e.g., for summary or translation). The Args/Returns/Raises format implies usage but does not give contextual cues.
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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- Evaluate tool definition quality.
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