WebScraper MCP Server
Server Quality Checklist
Latest release: v2.0.0
- Disambiguation5/5
Each tool has a distinct purpose: scrape_page retrieves page content, extract_links gets hyperlinks, and extract_images gets images. There is clear separation of concerns with no overlapping functionality.
Naming Consistency5/5All tool names follow a consistent verb_noun pattern in snake_case (scrape_page, extract_links, extract_images). While 'scrape' and 'extract' differ, they are appropriate verbs for their respective actions, and the pattern is uniform.
Tool Count5/5Three tools is well-scoped for a focused web scraping server. Each tool covers a core aspect of scraping without redundancy, fitting comfortably within the ideal 3-15 range.
Completeness4/5The set covers fundamental scraping needs: content, links, and images. It could be extended with custom selector support or structured data extraction, but the current surface handles basic workflows without dead ends.
Average 3.6/5 across 3 of 3 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
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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. It only states that scraping occurs and that Markdown is returned. It does not mention whether JavaScript rendering is enabled by default, how redirects are handled, rate limits, authentication requirements, or potential blocks. The extensive options in the schema are not reflected in the description.
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 two sentences long and front-loads the core purpose. It avoids unnecessary detail and remains efficient, though it could benefit from mentioning key options without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with a nested options object and no output schema or annotations, the description is too sparse. It leaves the agent uncertain about what 'metadata' and 'extracted information' entail, how the tool behaves on dynamic pages, and how it relates to the sibling extraction tools. The description would benefit from naming the available options and clarifying return shape.
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 schema provides complete descriptions for all parameters (coverage 100%), so the description does not need to explain them. The description's mention of 'extracted information' is vague but does not conflict with schema; however, it adds little detail beyond what the schema already documents.
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 action ('Scrape a web page and convert it to Markdown format') and its output (content, metadata, extracted information). It distinguishes itself from the sibling extractors (extract_links, extract_images) by emphasizing full-page conversion rather than specific element extraction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus the sibling tools (extract_links, extract_images). It does not specify scenarios or requirements such as needing only links or images, nor does it mention any prerequisites for scraping.
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?
No annotations are provided, so the description carries the full burden. It discloses the return format (list of links with text, URLs, metadata) but does not mention potential side effects, failure modes, or that it is a read-only operation. This is minimal but not misleading.
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 two sentences long, front-loaded with the core action, and every word adds value. It is concise and well-structured with no redundancy.
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?
For a simple one-parameter tool with no output schema, the description adequately covers purpose and return structure. However, it omits edge-case behavior like error handling or dynamic content, but this does not severely hinder usability for straightforward cases.
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?
Schema description coverage is 100%—the 'url' parameter is already fully described as 'The URL of the web page to extract links from.' The description repeats this without adding extra semantics, so it meets the baseline for high schema coverage.
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?
Description uses a specific verb+resource ('Extract all links from a web page') and clearly differentiates from sibling tools like extract_images and scrape_page by focusing on links. It also specifies the return structure (text, URLs, metadata), making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There are no explicit conditions, exclusions, or references to sibling tools, leaving the agent to infer usage solely from the tool name and purpose.
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?
No annotations are provided, so the description bears the burden of behavioral disclosure. It does state that it returns a list with image URLs, alt text, and metadata, which is a useful behavioral trait. However, it omits details about potential errors, rate limits, handling of dynamic content, or what 'all images' means in edge cases. This is adequate for a simple tool but not fully transparent.
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 consists of two concise sentences that directly state the action and the return value. There is no redundant or filler text, and it is front-loaded with the primary action. Every word earns its place.
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?
For a tool with a single required parameter and no output schema, the description provides a complete enough picture: it states what it does and what it returns. It lacks additional context about limitations or prerequisites, but given the simplicity, this is sufficient. A score of 4 reflects a well-rounded description for the tool's complexity.
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 schema already fully describes the single parameter (url) with 100% coverage, stating 'The URL of the web page to extract images from'. The description's phrase 'from a web page' adds no new semantics beyond the schema. Per the rubric, with high schema coverage, a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Extract' with the resource 'all images from a web page', which clearly states the tool's function. It distinguishes from sibling tools like extract_links by focusing specifically on images rather than links. The mention of return contents (URLs, alt text, metadata) further clarifies the purpose.
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 clearly implies when to use the tool (when images from a web page are needed), but it does not explicitly mention alternatives or exclusionary criteria. Since the context is straightforward and distinguishes from siblings through the tool name and description, it deserves a score above 'no guidance' but not the full 5 because it lacks explicit 'when not to use' language.
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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