read-website-fast
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'read_website' has a clear, distinct purpose focused on web content extraction.
Naming Consistency5/5The single tool name 'read_website' follows a clear verb_noun pattern. Since there is only one tool, consistency is inherently perfect with no deviations to assess.
Tool Count2/5A single tool is generally too few for most server purposes, as it limits functionality and can feel thin. While the tool is well-described, the server's scope might benefit from additional related operations (e.g., for processing or analyzing the extracted content).
Completeness3/5The tool covers the core function of reading websites effectively, but there are notable gaps for a broader web content domain. For example, there are no tools for updating, filtering, or managing multiple website reads, which could limit agent workflows.
Average 4.2/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 2 of 3 community issues answered or closed in the last 6 months
- 20 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it mentions 'fast, token-efficient' performance and output format ('converts to clean Markdown while preserving links and structure'), which are not covered by annotations like readOnlyHint or idempotentHint. Annotations already indicate safe, non-destructive operations, so the description complements this without contradiction, though it could add more on rate limits or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with key information ('Fast, token-efficient web content extraction') and uses two concise sentences that efficiently convey purpose, ideal use cases, and output format without any wasted words. Every sentence adds value, making it highly structured and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (3 parameters, no output schema), the description is largely complete: it covers purpose, usage context, and behavioral traits. However, it lacks details on output specifics (e.g., what the Markdown output looks like or error cases), which would be helpful since there's no output schema. Annotations provide safety info, but more output context could enhance completeness.
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 description does not explicitly discuss parameters, but the input schema has 100% description coverage, providing full details on 'url', 'pages', and 'cookiesFile'. This high coverage means the schema carries the burden, so the baseline score of 3 is appropriate as the description adds no additional parameter semantics beyond what the schema already explains.
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's purpose with specific verbs ('extraction', 'reading', 'analyzing', 'gathering') and resources ('web content', 'websites'), and distinguishes its functionality by mentioning conversion to clean Markdown while preserving links and structure. It explicitly differentiates from potential alternatives by highlighting 'fast, token-efficient' extraction, making the purpose highly specific and actionable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use this tool ('ideal for reading documentation, analyzing content, and gathering information from websites'), which helps guide usage. However, it does not explicitly state when not to use it or name alternatives, and with no sibling tools listed, there is no direct comparison. This results in strong but not exhaustive guidance.
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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