malcolm_aggregate

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It describes the tool's functionality but does not state whether it is read-only, if it requires authentication, or if it has any side effects. The tool appears non-destructive, but the description lacks explicit safety or authorization context.

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 approximately 100 words, with a clear structure: a one-sentence summary, a brief description of output, and an Args section. It is front-loaded with the core purpose and efficiently covers each parameter without redundancy. Every sentence adds value.

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 (5 parameters, output schema exists), the description covers the core functionality and parameter usage well. However, it lacks an explanation of the 'Malcolm filter syntax' used in the filters parameter, which could hinder correct usage. Otherwise, it is complete for an aggregation tool.

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%, meaning the description must fully explain parameters. It does so comprehensively for all 5 parameters: fields (with comma-separated examples), filters (JSON Malcolm syntax), limit (range 1-500), time_from, and time_to (dateparser format). Examples for fields provide strong guidance.

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 aggregates network traffic by one or more fields and returns bucket counts. It distinguishes itself from siblings like malcolm_search (raw records) and count (total count) by focusing on grouped frequency analysis. Examples of multi-level aggregation reinforce the specific resource and action.

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 usage for aggregating and counting field values, but does not explicitly contrast with alternatives like malcolm_search for raw data or arkime_unique for distinct values. The context and examples provide clear guidance for when to use this tool, but without explicit exclusions.

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