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. While it mentions analyzing 'sentiment and emotional tone,' it doesn't describe how the analysis is performed (e.g., model used, confidence scores), what the output format looks like, potential rate limits, or error handling for inaccessible URLs. This leaves significant gaps for a tool with no structured behavioral hints.

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 front-loads the core purpose without unnecessary elaboration. Every word contributes directly to understanding the tool's function, making it appropriately concise and well-structured for quick comprehension.

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 sentiment analysis (which involves nuanced text processing) and the absence of both annotations and an output schema, the description is insufficient. It doesn't cover behavioral aspects like output format, error conditions, or performance characteristics, leaving the agent with incomplete context for effective use.

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 parameters (url, granularity, useCache) with descriptions and defaults. The description adds no additional parameter semantics beyond what's in the schema, such as explaining the implications of different granularity levels or cache usage. This meets the baseline for high schema coverage.

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 as analyzing sentiment and emotional tone of web content, which is a specific verb+resource combination. However, it doesn't explicitly distinguish itself from sibling tools like 'analyze_readability' or 'classify_content' that might also analyze textual aspects, leaving some ambiguity about its unique role.

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., analyze_readability, classify_content, extract_keywords), there's no indication of whether sentiment_analysis is preferred for emotional assessment or how it complements other tools, leaving usage context unclear.

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