Fetch web page content, remove noise, and return cleaned text suitable for LLM context. Enables extracting relevant information like weather forecasts for further processing.
Extract page content, clean it, and return context-ready text for an LLM.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral burden, and it does so by disclosing the extraction, cleaning, and final text output. It does not enumerate potential limitations like truncation or source-page assumptions, but the stated behavior is clear and non-destructive in nature.
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 concise sentence states the operation, the transformation, and the output. Every word contributes information, 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?
Given the zero-parameter schema and low complexity, the description is complete enough for an agent to understand the tool's role. It explains the input source ('page') and the expected deliverable, though it leaves the exact page source implicit.
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 tool has zero parameters, and the schema already fully documents that. The description adds relevant context about the result format ('context-ready text for an LLM'), satisfying the 0-parameter baseline.
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?
Description uses a specific verb ('Extract'), identifies the resource ('page content'), and states the outcome ('return context-ready text for an LLM'). It is unambiguous and clearly differentiates from the weather-focused sibling tools.
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 the use case: use this tool when an LLM needs cleaned page content. It provides no exclusions, and the sibling tools are unrelated weather tools, so there is no real alternative-usage ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.