GrabzIt MCP Server
OfficialServer Quality Checklist
Latest release: v1.0.1
- Disambiguation4/5
The tools are mostly distinct: scrape_html extracts HTML, inspect_url captures a screenshot, and convert_url/convert_html convert to Image/PDF/DOCX. However, convert_url to image and inspect_url both produce images, which could cause minor confusion in tool selection.
Naming Consistency5/5All tool names follow a consistent grabzit_<verb>_<object> pattern (scrape_html, inspect_url, convert_url, convert_html). The verbs and objects are uniform, making the naming highly predictable.
Tool Count5/5With only 4 tools, the server is well-scoped for a web scraping and conversion service. Each tool covers a distinct conversion or extraction need without unnecessary bloat.
Completeness4/5The tool set covers key operations: HTML scraping, URL inspection, and conversion from both URL and raw HTML. Minor gaps exist such as no explicit text extraction or batch processing, but the core workflow for the domain is well-covered.
Average 3.2/5 across 4 of 4 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 12 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
This repository is licensed under ISC 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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states the basic conversion. It doesn't disclose behaviors like default format ('jpg'), delay handling, or possible limitations (e.g., output file type). Additionally, there is an internal inconsistency: description says 'raw HTML string' while schema says the 'html' parameter expects a URL.
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 a single, front-loaded sentence with no filler. Every word contributes to stating the core function.
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?
The tool has 7 parameters, 3 enums, and no output schema, but the description is too minimal to guide correct invocation. It doesn't mention key parameters, return behavior, or how to choose between output formats. A more detailed description is needed given the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3, but the description introduces ambiguity by calling input 'raw HTML string' while the schema says it's a URL. This contradicts the parameter description and reduces clarity rather than adding value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('Converts') and resource ('raw HTML string') with target formats ('Image, PDF, or DOCX'). It distinguishes from sibling tools like grabzit_convert_url by emphasizing raw HTML string input, though it doesn't explicitly compare to siblings.
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?
No guidance on when to use this tool versus alternatives like grabzit_convert_url or grabzit_scrape_html. The description implies usage for HTML string input but provides no context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure, but it only says 'Converts a URL'. It does not explain rendering behavior, potential delays, or how the output is returned. This is a significant gap for a conversion tool.
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 a single, front-loaded sentence with zero wasted words. It perfectly adheres to conciseness, though it may be too terse for other dimensions.
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?
Given no output schema and no annotations, the description should provide more context about the conversion process, return value, or limitations. It is inadequate for a tool with 7 parameters and multiple format options.
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%, so the baseline is 3. The description adds no meaning beyond what the schema already states; even the output types are already covered by the 'format' parameter enum.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Converts') and resource ('a URL') with the output types (Image, PDF, DOCX). It clearly indicates what the tool does but does not differentiate it from siblings like grabzit_convert_html, so it falls short of a 5.
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?
No guidance is provided on when to use this tool versus alternatives. The description does not mention any exclusions, prerequisites, or preferred scenarios, leaving the agent to infer usage solely from the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It mentions the output is an image the AI can see, but does not disclose any side effects, limitations (e.g., dynamic content failures), or whether it is a read-only operation. The description adds minimal behavioral context beyond the tool's name.
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 a single sentence that is clear and direct, with no wasted words. It conveys the essential purpose without unnecessary detail.
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 screenshot tool, the description covers the purpose and return type. Although there are no annotations or additional context, the schema fully documents parameters, and the description provides enough to understand the tool's function. Minor gaps exist around edge cases and usage context, but overall it is largely complete.
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 full coverage (100%) for all five parameters, so the description need not elaborate. The description does not add any extra semantics about parameter usage, fitting the baseline of 3.
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 it captures a screenshot of a URL and returns an image, using a specific verb and resource. This distinguishes it from sibling tools like grabzit_scrape_html (text scraping) and grabzit_convert_url (URL conversion).
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?
No guidance is provided on when to use this tool versus alternatives, such as when visual inspection is needed vs. textual extraction. There is no mention of alternatives, exclusions, or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It only mentions JavaScript execution, but does not state whether it makes network requests, is read-only, or how it handles failures. This is minimal behavioral disclosure.
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 a single sentence that front-loads the core purpose with no redundant or irrelevant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with two parameters and no output schema. The description conveys the main function but omits details on return format, error handling, or when to prefer other tools. It is adequate but not fully complete.
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%, so the description does not need to add parameter details. The schema already documents url and delay, and the description provides no extra semantic value.
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 extracts fully-rendered HTML after JavaScript executes, making the purpose specific and distinct from sibling tools like grabzit_convert_url or grabzit_inspect_url.
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?
The context of needing JS-rendered HTML is implied, but there is no explicit guidance on when not to use this tool or mention of alternatives. It lacks exclusions but provides a clear enough usage context.
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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