extract_webpage_text
Extract clean readable text from a public webpage or URL for AI agents, with core retrieval metadata.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| max_chars | No | ||
| force_browser | No |
SmartFetch
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
C2.6/5.0
Behavior2/5
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.
Conciseness4/5
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.
Completeness2/5
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.
Parameters1/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.
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TDQS
C2.6/5.0
Disambiguation1/5
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.
Naming Consistency3/5
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.
Tool Count2/5
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.
Completeness3/5
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.