Extract structured data from webpages by supplying URLs and a prompt describing the required information.
Extract structured data from webpages using LLM
| Name | Required | Description | Default |
|---|---|---|---|
| urls | Yes | URLs to extract data from | |
| prompt | Yes | Instructions for what data to extract |
Changes observed during successful MCP inspections.
v1.5.0Input schema / properties / enableWebSearchRemoved value: -{
- "default": false,
- "description": "Enable web search for additional context",
- "type": "boolean"
-}v1.3.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description carries the full burden. The phrase 'using LLM' is genuinely useful behavioral context — it signals non-determinism, cost, and latency. However, nothing is disclosed about failure behavior, result format, rate limits, or the read-only nature of extraction beyond what the verb 'extract' implies.
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 tight clause with the verb front-loaded and zero filler words. It is appropriately brief for a tool whose parameters are already well-documented in the schema, though it could afford a bit more substance.
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 two-parameter tool with complete schema coverage and no output schema, the description is mostly adequate on inputs. But it does not address the ambiguity with 'extract_with_schema', nor does it clarify what a successful extraction returns. These are notable gaps for a tool of moderate complexity.
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%, with both 'urls' and 'prompt' already described in the schema ('URLs to extract data from' and 'Instructions for what data to extract'). The tool description adds nothing beyond this, so it sits at the baseline of 3.
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 ('extract'), resource ('structured data from webpages'), and method ('using LLM'), so the core purpose is clear. However, it does not distinguish itself from the sibling 'extract_with_schema', which likely exists precisely for schema-driven extraction. Without differentiation, an agent cannot tell which variant to pick.
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 usage guidance is provided at all. The description does not say when to choose this tool over the closely related siblings (scrape_page, batch_scrape, extract_with_schema), nor does it mention any exclusions or prerequisites such as LLM cost or token limits.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.