MCP Web Scrape

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions classification but doesn't describe how it works (e.g., machine learning model, rules-based), what the output format looks like, error handling, rate limits, or authentication requirements. For a tool with no annotation coverage, this leaves significant behavioral gaps.

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, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for a tool with clear parameters documented in the schema and no complex behavioral nuances to explain.

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 no annotations and no output schema, the description is incomplete for a classification tool. It doesn't explain what the classification output looks like (e.g., confidence scores, multiple categories), how errors are handled, or performance characteristics. For a tool that presumably returns structured classification results, this leaves too much unspecified.

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%, so the schema already documents all three parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain what 'categories' classification entails or how 'useCache' affects performance). Baseline 3 is appropriate when 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: 'Classify web content into categories and topics'. It specifies the verb ('classify'), resource ('web content'), and output ('categories and topics'). However, it doesn't explicitly differentiate from siblings like 'detect_language' or 'sentiment_analysis' which also analyze content, though the classification focus is reasonably distinct.

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. With many sibling tools for content analysis (e.g., 'detect_language', 'sentiment_analysis', 'extract_keywords'), there's no indication of when classification is preferred over other methods or what specific scenarios it's designed for.

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