MCP Web Scrape

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 what data types are extracted but doesn't cover aspects like rate limits, authentication needs, error handling, or what the output looks like (e.g., format, structure). For a tool with no annotations, this is a significant gap in transparency about its operation and constraints.

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 extremely concise—a single phrase listing the data types extracted. It's front-loaded with the core purpose and wastes no words, making it easy to parse quickly. Every element in the description earns its place by specifying the tool's scope.

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 complexity of data extraction (3 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the output format, error conditions, or behavioral traits like performance or limitations. Without annotations or an output schema, the description should provide more context to guide effective use, but it falls short.

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 input schema has 100% description coverage, clearly documenting all parameters (url, dataTypes, useCache) with details like defaults and enums. The description adds no additional parameter semantics beyond what's in the schema, such as examples or edge cases. Given the high schema coverage, a baseline score of 3 is appropriate as the schema does the heavy lifting.

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's purpose: extracting JSON-LD, microdata, and schema.org data. It specifies the action ('extract') and the resource ('structured data'), though it doesn't explicitly differentiate from sibling tools like 'extract_schema_markup' or 'extract_content' beyond listing data types. This makes the purpose clear but not fully distinguished from alternatives.

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 alternatives. It doesn't mention sibling tools like 'extract_schema_markup' or 'extract_content', nor does it specify scenarios or prerequisites for use. This leaves the agent without explicit usage context, relying solely on the tool name and parameters.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.