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Advanced Device Search

advanced_device_search
Read-onlyIdempotent

Perform advanced device searches with structured queries to filter by software, tags, or inactivity status, enabling targeted device management.

Instructions

Execute an advanced device search using the Automox Advanced Device Search API's structured query language. Enables complex queries like 'find all Windows devices not seen in 30 days' or 'devices with nginx installed' using field-based filtering. Pass query as a dict with a filters list of AND/OR groups, each a list of conditions: {"filters": [{"AND": [{"scope": "SOFTWARE", "field": "pkgDisplayName", "operator": "IN", "values": ["nginx"]}]}]}. Tag search uses scope TAGS (not DEVICE): {"scope": "TAGS", "field": "tag", "operator": "IN", "values": ["Nginx"]}. Use get_searchable_fields for valid scope/field/operator combos and device_search_typeahead to discover values. The org is scoped automatically. limit sets the page size. On each returned device, outstanding_patch_severity distinguishes the string 'none' (assessed, no outstanding patches — clean) from JSON null/absent (device not yet assessed — unknown, NOT clean); see metadata.field_notes.outstanding_patch_severity. Use an org-scoped API key — global/account keys are unreliable on this endpoint and often return HTTP 403.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
limitNo
queryNo
output_formatNojson
Behavior5/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description adds significant context beyond these: explains the query structure (AND/OR groups), warns about API key scope issues, and details the interpretation of outstanding_patch_severity field (string 'none' vs null/absent). No contradictions with annotations.

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 appropriately sized, front-loaded with the core purpose, and each sentence serves a purpose. It follows a logical flow: purpose, example, query structure, warnings, field interpretation. No unnecessary repetition or filler.

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

Completeness4/5

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

Given the tool's complexity (structured query language, 4 parameters, no output schema), the description covers the query structure, key caveats (API key scope, field behavior), and references helper tools. It lacks explicit pagination details (e.g., how page works) and does not enumerate possible output_format values. However, the provided information is sufficiently complete for the intended advanced use case.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate. It provides extensive semantic meaning for the key parameter 'query', including the dict structure, examples for device and tag searches, and pointers to helper tools. 'limit' is mentioned as page size. However, 'page' and 'output_format' receive minimal explanation. Overall, it adds substantial value for the most complex parameter.

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 defines the tool as executing an advanced device search using a structured query language, with explicit examples of complex queries (e.g., 'find all Windows devices not seen in 30 days'). It distinguishes from sibling tools like list_devices and search_devices by emphasizing the advanced querying capability and referencing related helper tools (get_searchable_fields, device_search_typeahead).

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

Usage Guidelines5/5

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

Provides explicit guidance on when to use this tool (for advanced, complex queries) and when to use alternatives (simpler searches via list_devices or search_devices). It instructs users to leverage get_searchable_fields and device_search_typeahead for discovery. Also includes critical operational guidance: using an org-scoped API key (global keys may return 403) and noting that the org is scoped automatically.

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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