generate_relevance_chart

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

Without annotations, the description carries full burden. It discloses that a local HTTP server is started, the link is returned, and the message field contains markdown and raw URL. It does not mention potential side effects or error handling, but the core behavior is transparent.

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 structured with clear sections: main purpose, what charts are generated, server details, return format, and usage. It is slightly verbose but front-loaded with the key information.

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 2 parameters and output schema (not shown but present), the description covers input format, output behavior, and prerequisite call to analyze_relevance. Missing details about error cases or server availability, but sufficient for typical 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?

The input schema has no descriptions (0% coverage). The description fully compensates by explaining that 'topic' is the research topic description (chart title) and 'papers_json' must be the JSON string from analyze_relevance containing a 'ranked_papers' array.

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 generates multi-angle relevance analysis charts, launches a local HTTP server, and returns a link. It details three specific chart types (relevance bar chart, K-Means clustering scatter plot, TF-IDF keyword analysis), which distinguishes it from siblings like analyze_relevance or search_papers.

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 explicitly advises to first call analyze_relevance and then pass its JSON output to this tool. It provides a typical usage pattern but does not explicitly state when not to use it or mention alternative tools for other scenarios.

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