ubersuggest_keyword_research

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

With no annotations, the description carries full burden but only states what data is returned, not behavioral traits. It doesn't disclose whether this is a read-only operation, requires authentication, has rate limits, or how results are structured (e.g., pagination). For a tool with no annotations, this is a significant gap in transparency.

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 with zero waste—it directly states the tool's function and output data. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.

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. It doesn't explain return values, error conditions, or behavioral context, leaving gaps for a tool that likely involves external API calls. For a keyword research tool with three parameters, more context is needed to guide 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 three parameters (keyword, language, location) with clear descriptions. The description adds no additional meaning beyond implying these parameters might be used for research, which is redundant. Baseline 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 verb 'research' and the resource 'keywords', specifying the data returned (volume, difficulty, competition). It distinguishes from siblings like domain_overview or site_audit by focusing on keyword-level analysis. However, it doesn't explicitly contrast with traffic_estimation, which might also involve keywords.

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?

No guidance on when to use this tool versus the three sibling tools is provided. The description implies usage for keyword-level metrics, but there's no explicit mention of alternatives, prerequisites, or exclusions. This leaves the agent to infer context from tool names alone.

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