Extract structured JSON from any website using Edge AI. Bypasses all anti-bot protections automatically.
Scored across 2 tools
The two tools target clearly different operations: opticparse_scrape extracts page content, while phishvision_detect audits URL threat status. There is no practical scenario in which an agent would confuse them.
Both names follow the same lowercase underscore pattern: product-prefix followed by a verb (scrape/detect). Slight deviation from a pure verb_noun convention, but the pattern is predictable.
Two tools is borderline; it feels thin for a server claiming broad scraping and phishing detection capabilities, but each tool is meaningful and the set is not bloated.
The domain appears to be web intelligence/URL analysis, but the surface lacks common supporting operations such as URL validation, batch processing, history/reporting, or phishing remediation. For the broad advertised scope, notable workflows will hit dead ends.
Extract structured, token-optimized data from any live web page using AI Multimodal Vision. Bypasses Cloudflare Turnstile, anti-bot mechanisms, and dynamic JavaScript rendering without brittle CSS selectors. Perfect for LLM context windows and RAG pipelines.
| Name | Required | Description | Default |
|---|---|---|---|
| target_url | Yes | The fully-qualified HTTP/HTTPS URL of the webpage to scrape and extract content from. | |
| response_schema | No | Optional JSON Schema definition to enforce a strict structured output format on the extracted result. | |
| extraction_query | Yes | Natural language instructions specifying what data fields, tables, or text to extract from the webpage. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
It discloses meaningful behaviors such as bypassing Cloudflare Turnstile, anti-bot mechanisms, and dynamic JavaScript rendering, while avoiding CSS selectors. However, with no annotations at all, the description carries the full burden for behavior and does not clarify output format, failure modes, rate limits, or legal/auth constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three focused sentences with the core capability front-loaded. The final 'perfect for LLM context windows and RAG pipelines' is slightly promotional but still conveys appropriate use cases without significant bloat.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the tool description should clarify what the agent can expect back. 'Structured, token-optimized data' is a helpful hint but does not define whether the result is JSON, text, or an object respecting response_schema. Overall adequate for a straightforward invocation but incomplete on return and error behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters already have descriptive schema entries, so the description adds no extra paramater-level meaning. It reinforces the general extraction behavior but does not clarify specifics like how response_schema interacts with the output or what form extraction_query should take beyond 'natural language'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states a specific action ('Extract structured, token-optimized data') and a specific resource ('any live web page'), making the tool's purpose obvious. It doesn't explicitly contrast with the sibling phishvision_detect, but the extraction-focused language is enough to distinguish it from a detection tool. The naming and description align well.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives useful context by highlighting anti-bot bypass, dynamic JS rendering, and suitability for LLM/RAG pipelines, which implies when it should be used. It does not explicitly state when to prefer an alternative or when not to use it, leaving the routing decision partially inferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Audit and inspect any URL for real-time zero-day phishing campaigns, smart contract wallet drainers, credential harvesting kits, and brand impersonation attacks using visual layout heuristics in under 1.6 seconds.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The target domain or fully qualified URL to audit for security threats, drainers, and malicious vectors. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does disclose useful behavioral details: it uses visual layout heuristics and completes in under 1.6 seconds. However, it does not clarify side effects such as whether the tool actively fetches or renders the target URL, whether it is strictly read-only, or what happens when the URL is malicious or unreachable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The entire description is one front-loaded sentence that opens with the primary action and resource, then adds precise threat categories, method, and a performance bound. There is no filler or redundant restatement of the tool name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema and no annotations, the description covers the main selection and invocation needs: target input, detection scope, method, and speed. It could be more complete by indicating the return shape or verdict format, but that is a minor gap given the low complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents url as 'The target domain or fully qualified URL to audit for security threats, drainers, and malicious vectors.' The description reinforces the purpose but does not add new parameter-level meaning such as format restrictions or accepted schemes.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb-resource pair ('Audit and inspect any URL') and enumerates concrete detection targets: phishing campaigns, wallet drainers, credential harvesting kits, and brand impersonation. This clearly differentiates it from the sibling opticparse_scrape, which by name is oriented toward extraction rather than security inspection.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when the tool applies: real-time URL security auditing with visual layout analysis. It does not explicitly state exclusions or name opticparse_scrape as the alternative, so it stops short of a 5, but the use case is unmistakable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
opticparse_scrapephishvision_detect
Add one secure layer between your agents and this server.
