GNQL Query

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

Annotations include readOnlyHint=true and openWorldHint=true. The description adds value by detailing that results are paginated and the use of a scroll token. It also lists available facets and query examples, giving the agent a clear understanding of the tool's behavior beyond the annotations.

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 well-structured with a clear opening, bullet-pointed facets, and examples. It is comprehensive but not overly verbose. All content is relevant and adds value. Minor room for tightening, but overall efficient.

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 tool's complexity (query language, pagination, facets), the description covers all necessary aspects: what the tool does, how to use GNQL, available fields, example queries, and pagination. An output schema exists, so return values are documented separately. The description is complete for an agent to use the tool correctly.

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 100% description coverage for the three parameters (query, size, scroll). The description adds some context by explaining GNQL syntax and providing examples but does not significantly enhance parameter understanding beyond what the schema already provides. Baseline score is appropriate.

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: 'Search GreyNoise data using GNQL (GreyNoise Query Language). Returns full IP context results including raw scan data.' This distinguishes it from sibling tools like gnql-stats (aggregations) and gnql-timeseries (time series). The verb 'Search' with specific resource and output is precise.

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 explains the GNQL language, provides examples, and notes pagination. However, it does not explicitly state when to use this tool vs. alternatives (e.g., gnql-stats for counts, gnql-metadata-query for metadata). The user can infer context from the examples, but explicit guidance is missing.

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