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.
Fetch the fully rendered HTML of any web page through the ScrapeUnblocker API (https://developers.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?
Even with readOnlyHint/openWorldHint annotations, the description adds meaningful behavioral context: bypassing anti-bot systems, running steps in a real browser, and the steps schema discloses non-idempotency ('runs once and is not retried') and the 422 failure response with the offending step and page HTML. No contradiction with the 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?
The main description is four tight sentences, front-loaded with the core operation, then use case, output, and interaction workflow. The API link and selector-discovery tip are relevant, and there is no filler or repetition of the schema.
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 6-parameter tool with one nested object and no output schema, the description plus the schema covers the essential context: output ('raw HTML'), failure mode (422), selector discovery, and when to use it. It is slightly less complete because it never contrasts with fetch_parsed, and the main description leaves wait_method/wait_value usage to the schema.
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 every parameter, making 3 the baseline. The tool description adds a little context by framing steps as 'search, click, paginate' and pointing to list_elements, but it does not add meaning beyond the schema for wait_value, proxy_country, or sleep_seconds.
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 opens with a specific verb and resource: 'Fetch the fully rendered HTML of any web page' and adds the ScrapeUnblocker/anti-bot context. It says 'Returns raw HTML,' which hints at the difference from the sibling fetch_parsed, but it never explicitly names or contrasts that sibling, so the differentiation is left to inference.
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?
It gives an explicit trigger condition: 'Use when a normal fetch is blocked (403/429, captcha) or the page needs a real browser.' It also supplies a recommended workflow: use list_elements first to discover selectors before passing steps. It does not explicitly say when to prefer fetch_parsed, so full when-not guidance is missing.
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://developers.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 openWorldHint. The description adds context about using the ScrapeUnblocker API and returning parsed JSON, but doesn't disclose additional behavioral traits such as rate limits or what happens on failure.
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, efficient sentence that front-loads the core purpose. It is concise without being overly terse, though breaking into multiple sentences could improve readability.
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 2 parameters, no output schema, and openWorldHint, the description adequately explains the input (URL) and output (structured JSON). It could mention that output format depends on page type, but overall it is sufficient.
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% and both parameters have descriptions. The description does not add any parameter-specific semantic information beyond what is already in the schema, so baseline 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 specific verb 'Fetch', the resource 'web page', and the output 'AI-parsed structured JSON', which distinguishes it from siblings like fetch_html (raw HTML) and google_search (search results).
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 phrase 'instead of raw HTML' implies when to use this tool versus fetch_html, but no explicit exclusions or alternative guidance for google_search are provided. Usage context is clear, but not fully explicit.
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://developers.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?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds minimal behavioral context beyond stating it runs through the ScrapeUnblocker API. It does not mention rate limits, pagination traits, or response size, but annotations suffice for safety profile.
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?
A single 15-word sentence that is front-loaded with the core action. Every word is necessary and no filler.
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, the description does not detail the JSON structure or pagination behavior. For a search tool with 3 parameters, it adequately states the purpose but leaves the return format undocumented, which is a gap for an agent.
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%, so the baseline is 3. The description does not add meaning beyond what the schema already provides for parameters like keyword, proxy_country, or pages_to_check. No extra guidance on formatting or use.
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 verb 'run', the resource 'Google search', and the output format 'organic results as JSON'. It distinguishes itself from siblings 'fetch_html' and 'fetch_parsed' by specifying the source (Google) and the structured return type.
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 obtaining Google search results but does not explicitly contrast with sibling tools or provide when-not-to-use guidance. An agent must infer that this tool is specific to Google searches vs general web scraping from siblings.
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://developers.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?
With readOnlyHint and openWorldHint already signaling a safe, externally-fetching read operation, the description adds meaningful context: it returns ready-to-use selectors rather than raw HTML and positions the tool as a discovery step. It does not discuss rate limits or API credentials, but the annotation coverage lowers the bar and the added output behavior is valuable.
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 carry the full meaning with no filler. The main behavior and output format are front-loaded, and the second sentence provides a concrete usage workflow and sibling reference. The API link in the first sentence is useful rather than decorative.
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?
Despite having no output schema, the description supplies a JSON skeleton and enough semantic detail to know what each element object provides (a selector). It also names the relevant sibling workflow. It does not fully specify the inner shape of each element or behavior across all five params, but the schema descriptions cover the parameter side.
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 baseline is 3. The tool description does not add param-specific explanations, but the schema fully documents url, wait_method, wait_value, proxy_country, and sleep_seconds. The mention of 'ready-to-use selector' hints at output semantics rather than input parameter meaning.
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 names the resource ('interactive elements'), the action ('Fetch a page... and return'), and the output format ('JSON {url, count, elements:[...]} instead of raw HTML'). It also names fetch_html, which distinguishes it from a sibling tool that returns different content.
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 explicit context for when to use the tool: 'Use it to discover what to target, then drive the page with the `steps` param of fetch_html.' This effectively routes the agent to a sibling for the follow-up action, but it does not explicitly state when not to use the tool or compare against fetch_parsed or google_search.
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. Dates show when Glama detected each change.
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_searchClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Add one secure layer between your agents and this server.
Each tool has a clear, distinct purpose: fetch_html returns raw HTML, fetch_parsed returns structured JSON, and google_search performs a search. No overlap.
All tool names follow a consistent verb_noun pattern using lowercase with underscores (fetch_html, fetch_parsed, google_search).
Three tools is a reasonable count for a focused scraping API, covering core functionality without being sparse or excessive.
The set covers fetching raw HTML, parsed data, and Google search, but lacks features like custom headers, session management, or other search engines, leaving notable gaps for advanced use cases.