Paid fallback for public webpages: clean text, Markdown, links, metadata, and JavaScript rendering.
Glama connects to this endpoint on a schedule and records what it finds.
Last verified
Scored across 4 tools
All four tools describe essentially the same operation: fetching or extracting a webpage and returning clean text/Markdown. fetch_webpage explicitly includes browser rendering and Markdown output, so it overlaps with every other tool and leaves no clear basis for selecting among them.
Most names follow a verb-first snake_case pattern (fetch_webpage, render_webpage, extract_webpage_text), but webpage_to_markdown breaks the pattern by using a noun_to_noun form. The naming is readable but not fully consistent.
Four tools is not inherently excessive, but the set is highly redundant for a narrow fetch/extract purpose; one comprehensive tool or at most a two-tool split would be more appropriate. The current count inflates the surface without adding distinct capabilities.
The core job of fetching and converting public webpages to text/Markdown is covered, including browser rendering for JavaScript-heavy pages. However, the surface is so overlapping that it lacks meaningful capability stages or distinct output modes beyond the bundled text/Markdown/metadata payload.
Extract clean readable text from a public webpage or URL for AI agents, with core retrieval metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| max_chars | No | ||
| force_browser | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of disclosing behavior. It only mentions 'clean readable text' and 'core retrieval metadata', but does not explain how the tool handles non-public pages, dynamic content, redirects, rate limits, or what the retrieval metadata actually contains. This is inadequate for an agent to anticipate side effects or failure modes.
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, clear sentence that immediately states the core function. It is front-loaded and contains no filler, making it efficient and well-structured for a brief overview.
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 three parameters, no output schema, and no annotations, the description only provides a high-level summary. It does not explain parameter semantics, return format, or edge cases, leaving an agent without enough information to properly invoke the tool. More details on the parameters and the retrieval metadata are necessary.
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 0%, and the description does not reference any parameters. The agent receives no explanation of what max_chars or force_browser control or how to use them correctly. The description fails to compensate for the schema's lack of detailed parameter meanings.
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 specifies a verb (extract), a resource (webpage text), and an intended audience (AI agents), and hints at processing into 'clean readable text'. It implicitly differentiates from siblings like fetch_webpage or render_webpage by focusing on text extraction, but it does not explicitly name alternatives or contrast them.
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 provides no guidance on when to choose this tool over its siblings (fetch_webpage, render_webpage, webpage_to_markdown). It only states what it does without any selection criteria, prerequisites, or exclusions, leaving the agent to guess the appropriate use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read, fetch, scrape, or extract any public webpage or URL for AI agents. Returns clean text, Markdown, links, and metadata, with automatic browser rendering for JavaScript-heavy pages.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| max_chars | No | ||
| force_browser | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the behavioral burden. It discloses that it works on public pages and handles JavaScript via auto-rendering, which is useful. However, it does not mention potential failure modes, authentication requirements, rate limits, or whether the tool follows redirects. The safety profile of a read operation is implied but not explicit.
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 two sentences and gets to the point immediately, listing the core output types. It is front-loaded with the primary verb and resource. It could have been more structured with a list of features, but it is efficient and free of 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?
For a tool with three parameters and no output schema or annotations, the description is incomplete. It does not explain how max_chars affects output, what force_browser does exactly, or the format of the returned links and metadata. An agent would need to guess or experiment to use advanced features 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 0%, and the description does not explain the three parameters (url, max_chars, force_browser) at all. The agent must rely solely on the schema's names and constraints, which are minimal. The description adds no value to parameter understanding, failing to compensate for the schema's lack of descriptions.
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 reads/fetches/scrapes webpages and returns text, Markdown, links, and metadata. While it lists multiple synonymous verbs, the core purpose is unambiguous. However, it does not explicitly differentiate from siblings like extract_webpage_text or webpage_to_markdown, so it's not a perfect 5.
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?
No explicit guidance on when to use this tool versus the listed siblings. The mention of 'automatic browser rendering for JavaScript-heavy pages' implies coverage, but there's no 'use this when...' or 'for text-only extraction use...' direction. The agent is left to infer suitability.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Browser-render a public JavaScript-heavy webpage or URL, then return clean text, Markdown, links, and metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| max_chars | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It reveals that the tool uses a browser, targets public pages, and returns text, Markdown, links, and metadata. Yet it does not mention latency, potential failures on heavy pages, output truncation behavior, or link handling beyond returning them.
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 sentence front-loads the method, target, and outputs. Every phrase contributes to selection or invocation understanding, with 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 and no annotations, the description needs to explain both return values and invocation constraints. It lists the key return types and the public-JS-heavy scope, but it omits max_chars semantics and any caveats about rendering failures or performance. This is enough for a simple call but not fully robust.
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 0%, so the description must compensate. It enriches the meaning of 'url' by specifying 'public JavaScript-heavy webpage or URL,' but it says nothing about 'max_chars' or how the returned content is capped. One of two parameters remains semantically under-explained.
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 uses a specific verb ('browser-render') with a specific resource ('public JavaScript-heavy webpage or URL') and states the deliverables: clean text, Markdown, links, and metadata. The 'JavaScript-heavy' qualifier differentiates it from siblings that likely fetch static or raw 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 clearly implies this tool is for JavaScript-heavy pages where ordinary fetching/extraction would be insufficient. However, it does not explicitly name alternative tools or state when not to use this one, so it stops short of full routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert a public webpage or URL into clean Markdown for AI agents, with core retrieval metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| max_chars | No | ||
| force_browser | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool converts public webpages to Markdown, but does not disclose whether it uses a headless browser (despite the force_browser parameter), any authentication requirements, rate limits, or what 'core retrieval metadata' actually includes. The behavior is underexplained, leaving the agent uncertain about side effects or prerequisites.
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 that states the core purpose without fluff. It is efficiently worded and the key action and resource are front-loaded. However, it is so brief that it omits critical details, but that is a completeness issue rather than a conciseness one.
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?
Given the tool has 3 parameters, no annotations, no output schema, and no parameter descriptions, the description is severely incomplete. It does not explain the max_chars and force_browser parameters, nor what 'core retrieval metadata' means or what the output structure is. An agent cannot invoke this tool correctly without additional knowledge, so this fails the completeness bar.
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 has 0% description coverage and the description does not mention any of the three parameters. The agent cannot learn what max_chars controls (character limit), what force_browser does (browser rendering), or any constraints beyond the schema titles. The description completely fails to add meaning to the schema, which is unacceptable given the low coverage.
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 'Convert' and the resource 'public webpage or URL', with an explicit output 'clean Markdown'. It is specific enough to tell the agent what the tool does, but it does not differentiate from sibling tools like extract_webpage_text or fetch_webpage, which might also convert web content. The mention of 'core retrieval metadata' hints at a distinguishing feature but does not elaborate.
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 provides no guidance on when to use this tool versus the siblings. There is no mention of scenarios where this is preferred over extract_webpage_text, fetch_webpage, or render_webpage. The agent is left to infer from the tool name alone, which is insufficient for correct selection.
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.
extract_webpage_textfetch_webpagerender_webpagewebpage_to_markdownPDFs, JavaScript pages and ordinary HTML as clean Markdown. Follows robots.txt per RFC 9309.
Read any web page as clean Markdown for AI agents: fetch, search, metadata, links. SSRF-safe.
Fetch any URL and get clean Markdown. Web scraping for AI agents.
Web scraping to clean Markdown with JS rendering, multi-page crawl, structured extract, sitemaps.
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