gsc_long_tail_queries

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

Without annotations, the description explains that it pulls queries, filters by min_words, sorts by impressions, and returns results. It is transparent about the filtering and sorting logic, though it does not detail rate limits or error states.

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 concise, front-loading the purpose and then listing parameters. A few words could be trimmed, but overall it is efficient and well-structured.

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 5 parameters and no annotations, the description covers purpose, filtering, sorting, and parameter formats. The output schema exists, so return values are documented elsewhere. No gaps are evident.

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 adds value by specifying date formats (YYYY-MM-DD), country code format (3-letter), and default values. It compensates well for the missing schema descriptions.

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 extracts long-tail, conversational GSC queries and positions them as a proxy for LLM inputs. It distinguishes from siblings by focusing on multi-word, natural-language queries rather than keyword searches.

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 implies use for GEO content strategy by linking queries to natural-language problem statements. It does not explicitly exclude other GSC tools or state when not to use, but the specific focus on long-tail queries provides clear guidance.

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