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search_dsl

Run raw OpenSearch DSL queries to retrieve full response with hits and aggregations. Time filter is controlled solely by the DSL body, with no hidden default window.

Instructions

Run a raw OpenSearch DSL query. Returns the raw response (hits + aggregations). Aggregations honor the time filter inside the DSL body, so there is no hidden default time window.

Args: index: index or pattern (e.g. "arkime_sessions3-*"). query_dsl: JSON string of a full DSL body ({"query": {...}, "aggs": {...}}). size: max hits (0 for aggregation-only). Always wins over a "size" key inside query_dsl.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNo
indexYes
query_dslYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It notes that the response is raw, that size always overrides the DSL body, and that there is no hidden time window for aggregations. This is relatively transparent, though it could mention any destructive potential or authentication requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with no redundant sentences. It starts with a clear purpose statement, adds a key behavioral note, then lists parameters in a structured Args block. Every sentence adds value, and the format is easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity, the presence of an output schema (which covers return values), and the 0% schema description coverage, the description adequately covers purpose, behavioral quirks, and parameter details. It leaves no major gaps for an agent to understand tool usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, but the description adds rich semantics for all three parameters: index with a pattern example, query_dsl as a JSON string with structural hints, and size clarifying it overrides the DSL body and zero is for aggregation-only. This fully compensates for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'Run a raw OpenSearch DSL query' and specifies it returns 'raw response (hits + aggregations)', making the action and output explicit. It effectively distinguishes this from sibling tools like 'malcolm_search' which likely provide higher-level search interfaces.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains that aggregations honor the time filter inside the DSL body with no hidden default, which clarifies behavior. However, it provides no explicit guidance on when to use this tool versus alternatives (e.g., malcolm_search), nor when not to use it, leaving the agent without clear usage boundaries.

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

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