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 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
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.
Add one secure layer between your agents and this server.
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.