research

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

With no annotations, the description carries the burden well: it discloses keyless public sources, Reddit access limitations, fallback behavior, and that the tool writes research.md, research.json, and voices.json under a scenario path. It does not cover overwrite behavior or error handling beyond Reddit blocking.

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 dense but front-loads the purpose, then sources, outputs, and usage. The single long sentence with many parentheticals is somewhat run-on, but most content earns its place.

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?

It provides useful context for a complex tool with no annotations or output schema by naming sources, output files, and sequencing. However, it leaves key parameter meanings unexplained, which is a significant gap given 6 parameters and 0% schema description coverage.

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 0%, so the description must compensate. It partially maps query, country, and urls, but does not explain the out, sources, or subreddits parameters, leaving half the parameters undocumented.

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 resource: public sentiment from Hacker News, App Store reviews, Reddit search, and page URLs, and names the output files. It is more specific than the tool name alone but does not explicitly distinguish itself from similar research-oriented siblings such as understand or study.

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?

It gives a clear sequencing rule ('Run it before planning personas') and a practical fallback for Reddit blocking: use browser tools and add findings as seed files. It lacks explicit when-not-to-use guidance or comparisons to sibling tools.

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