Fetch any web page's fully rendered HTML, AI-parsed structured JSON, or Google search results through ScrapeUnblocker's anti-bot scraping API (bypasses Cloudflare, DataDome, PerimeterX, Akamai). Bring your own API key.
Scored across 4 tools
fetch_html and fetch_parsed both fetch a URL, so an agent could initially be unsure whether to request raw HTML or structured JSON; however, the descriptions clearly separate output formats and interaction use cases. list_elements and google_search target different resources (page elements vs search results), so overall only mild overlap exists.
Two tools use a fetch_<format> pattern, and list_elements follows verb_noun, but google_search reads as a noun phrase rather than a consistent imperative. All names are lowercase snake_case and readable, so the convention is not broken.
Four tools cover the server's scraping/unblocking purpose without redundancy: raw HTML, parsed JSON, element discovery, and Google search. Each earns its place and the count is within the ideal 3-15 range.
The core scraping workflow is covered: discover elements, render/fetch HTML, interact via steps, extract structured data, and search Google. A minor gap is that fetch_parsed doesn't document an interaction/step path, so an agent that needs parsed data after clicking or logging in may have to fetch raw HTML and miss direct structured extraction.
Fetch the fully rendered HTML of any web page through the ScrapeUnblocker API (https://docs.scrapeunblocker.com), bypassing anti-bot protection (Cloudflare, DataDome, PerimeterX, Akamai, Shape). Use when a normal fetch is blocked (403/429, captcha) or the page needs a real browser. Returns raw HTML. Pass steps to interact with the page (search, click, paginate) before capture - use the list_elements tool first to discover selectors.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The absolute URL to fetch (http/https). | |
| steps | No | Ordered browser actions to run in a real browser after the page loads (wait_for, wait_for_text, wait, click, type [human-like], select, press_key, scroll), then return the resulting HTML. NON-IDEMPOTENT: it runs once and is not retried. A failed step returns a 422 naming the offending step plus the page HTML at that point. Discover selectors with list_elements first. | |
| wait_value | No | The selector/expression paired with wait_method. | |
| wait_method | No | Optional render-wait: 'css' selector or 'js' expression. | |
| proxy_country | No | Optional ISO country code to route through, e.g. 'US'. | |
| sleep_seconds | No | Extra seconds to wait after load. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds behavioral context like bypassing anti-bot protection and using a real browser, which helps the agent understand what happens under the hood. It also notes that steps are non-idempotent in the schema, though not in the main description—still, it adds enough value beyond annotations.
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?
Two sentences, front-loaded with the core purpose and key differentiator (anti-bot bypass). The alternative-trigger guidance and list_elements hint are placed efficiently without redundancy. Every sentence earns its place.
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 tool with a detailed schema and read-only annotations, the description covers the essential points: what it does, when to use it, what it returns, and how to prepare for interactive steps. No critical missing information for an agent to invoke it correctly.
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 each parameter is documented. The description adds a small amount of guidance for the steps parameter ('interact with the page... use the list_elements tool first'), but this is a usage hint rather than new semantic meaning. Baseline 3 is appropriate; the schema does the heavy lifting.
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 the specific action ('Fetch the fully rendered HTML of any web page') and resource (web page via ScrapeUnblocker API). It differentiates from siblings like fetch_parsed (raw vs parsed HTML) and google_search (search vs fetch). No ambiguity about what the tool does.
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?
Explicitly tells when to use: 'Use when a normal fetch is blocked (403/429, captcha) or the page needs a real browser.' It also directs the agent to use list_elements first for steps, providing clear alternative tool usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a web page through the ScrapeUnblocker API (https://docs.scrapeunblocker.com) and return AI-parsed structured JSON instead of raw HTML (product details, article content, listings).
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The absolute URL to fetch and parse. | |
| proxy_country | No | Optional ISO country code, e.g. 'US'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and destructiveHint, covering basic safety. The description adds that it returns AI-parsed JSON and references an external API, but does not mention side effects, rate limits, or error behavior. Given the annotations, the bar is lower, and the additional context 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the core action and output. It avoids redundancy, includes a useful API link, and is concise with no unnecessary words.
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?
The description gives a clear idea of the output (structured JSON) with examples, and references documentation for further context. It does not explain error handling or edge cases, but for a simple fetch-and-parse tool, this is adequate. The lack of an output schema is partially compensated by the examples provided.
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?
The schema provides 100% coverage of both parameters (url and proxy_country) with descriptions. The tool description does not add extra semantic detail beyond what the schema already states, so the baseline score of 3 is appropriate.
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 clearly states the tool fetches a web page and returns AI-parsed structured JSON, contrasting with raw HTML. It explicitly distinguishes from the sibling fetch_html tool by highlighting the structured output, making the purpose unambiguous.
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 implies usage for cases where structured data is needed by stating 'instead of raw HTML' and listing example content types (product details, article content, listings). However, it does not explicitly name sibling tools or provide precise conditions for when to choose this over google_search or list_elements, so it falls short of fully explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run a Google search through the ScrapeUnblocker API (https://docs.scrapeunblocker.com) and return organic results as JSON.
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | Yes | The search query. | |
| proxy_country | No | Optional ISO country code to search from, e.g. 'US'. | |
| pages_to_check | No | How many result pages to collect (default 1). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already indicate read-only, open-world, and non-destructive behavior. The description adds that it uses an external API but does not disclose further behavioral details such as rate limits, network dependencies, or potential result variance.
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 description is a single, concise sentence with no redundancy or irrelevant information. It clearly communicates the action, the method, and the expected output format.
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?
The description provides essential context: it performs a search, uses a specific API, and returns JSON with organic results. While there is no output schema, the description gives enough shape for a typical search-result response and the parameter schema fills in the remaining details.
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 coverage is 100%, with each parameter having a clear description, so the description does not need to repeat parameter details. The schema itself adequately documents keyword, proxy_country, and pages_to_check.
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 clearly states the tool runs a Google search via the ScrapeUnblocker API and returns organic results as JSON. This distinguishes it from sibling tools like fetch_html or list_elements, which are more generic scraping/list operations.
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 implies usage when Google search results are needed and specifies the output format, but it does not explicitly state when to use this tool versus the sibling tools or mention limitations. There is no direct alternative comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a page through the ScrapeUnblocker API (https://docs.scrapeunblocker.com) and return its interactive elements (buttons, inputs, selects, links, forms), each with a ready-to-use selector, as JSON {url, count, elements:[...]} instead of raw HTML. Use it to discover what to target, then drive the page with the steps param of fetch_html.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The absolute URL to load and inspect (http/https). | |
| wait_value | No | The selector/expression paired with wait_method. | |
| wait_method | No | Optional render-wait: 'css' selector or 'js' expression. | |
| proxy_country | No | Optional ISO country code to route through, e.g. 'US'. | |
| sleep_seconds | No | Extra seconds to wait after load before inspecting. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds concrete behavioral context: it fetches via ScrapeUnblocker API, returns a specific JSON structure {url, count, elements:[...]} with ready-to-use selectors, and that it replaces raw HTML. It doesn't cover error cases or rate limits, but the safety profile is already annotated, so a high score is warranted.
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?
Two sentences, no wasted words. The purpose is front-loaded, followed by the output format and usage guidance. Every clause contributes to the agent's decision-making.
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?
No output schema exists, so the description compensates by specifying the return JSON shape ({url, count, elements:[...]}) and that selectors are ready-to-use. It covers the essential workflow (discover then drive). Minor omissions like pagination or error behavior are acceptable for a read-only inspection tool.
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 fully documents all 5 parameters. The description does not add parameter-level detail beyond the schema, but it does mention the `steps` param of fetch_html as a follow-up, which is tangential. Baseline 3 is appropriate given the schema's thoroughness.
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 ('Fetch a page... return its interactive elements') and resource (ScrapeUnblocker API). It explicitly contrasts with raw HTML and names the sibling fetch_html, making it clear this tool is for element discovery rather than general page fetching.
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?
Provides explicit usage direction: 'Use it to discover what to target, then drive the page with the `steps` param of fetch_html.' This tells the agent when to use this tool and points to the alternative for subsequent actions, leaving no ambiguity.
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.
fetch_html1 field changedInput schema / properties / stepsAdded value: +{
+ "description": "Ordered browser actions to run in a real browser after the page loads (wait_for, wait_for_text, wait, click, type [human-like], select, press_key, scroll), then return the resulting HTML. NON-IDEMPOTENT: it runs once and is not retried. A failed step returns a 422 naming the offending step plus the page HTML at that point. Discover selectors with list_elements first.",
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "action": {
+ "description": "The action to perform.",
+ "enum": [
+ "wait_for",
+ "wait_for_text",
+ "wait",
+ "click",
+ "type",
+ "select",
+ "press_key",
+ "scroll"
+ ],
+ "type": "string"
+ },
+ "clear": {
+ "description": "For 'type': clear the field first.",
+ "type": "boolean"
+ },
+ "selector": {
+ "description": "CSS selector the action targets (required for wait_for/click/type/select).",
+ "type": "string"
+ },
+ "selector_type": {
+ "description": "How to interpret `selector` (default 'css').",
+ "enum": [
+ "css",
+ "xPath",
+ "className",
+ "tagName"
+ ],
+ "type": "string"
+ },
+ "timeout_ms": {
+ "description": "Per-step timeout override in ms.",
+ "exclusiveMinimum": 0,
+ "type": "integer"
+ },
+ "value": {
+ "description": "Action payload: text to type/select, text for wait_for_text, a key name for press_key (e.g. 'Enter'), milliseconds for wait, or 'bottom'/pixels for scroll.",
+ "type": [
+ "string",
+ "number"
+ ]
+ }
+ },
+ "required": [
+ "action"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+}list_elementsfetch_htmlfetch_parsedgoogle_search
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