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Asset Inventory Summary

inventory_summary
Read-onlyIdempotent

Retrieve unified asset inventory counts by type and source group, excluding findings. Covers AI, cloud, Snowflake, and identity assets.

Instructions

Return unified asset-inventory counts by type and source group.

    Counts every non-finding asset in the tenant's current graph snapshot —
    AI (agents, MCP servers, models, tools, credentials), cloud (resources,
    data stores, accounts), Snowflake, and identity — bucketed by OCSF entity
    type and by operator-facing group (ai / cloud / identity / secrets /
    code). Findings (CVEs, misconfigurations) are excluded; use blast_radius
    or exposure_paths for the finding queue.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scan_idNoOptional graph scan ID. Omit to use the latest snapshot.
tenant_idNoTenant scope for the snapshot. Defaults to 'default'.default

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, indicating a safe, read-only operation. The description adds behavioral context by specifying that it counts assets from the 'current graph snapshot', lists the specific asset types, and clarifies the exclusion of findings. This adds value beyond the annotations without contradiction.

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 and well-structured. It starts with a one-line summary, then elaborates on scope and grouping, and ends with exclusion and alternatives. Every sentence adds value without redundancy.

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 presence of an output schema (not shown but indicated), the description does not need to explain return values. It covers the input parameters, the scope of the count, the grouping dimensions, and what is excluded. It is complete for the tool's complexity.

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

Parameters3/5

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

The input schema has 100% coverage with descriptions for both parameters (scan_id and tenant_id). The description does not add significant new semantics beyond what the schema already provides. It mentions 'Omit to use the latest snapshot' but this is also present in the schema. Baseline 3 is appropriate.

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's purpose: returning unified asset-inventory counts by type and source group. It specifies that it counts only non-finding assets and lists the categories (AI, cloud, Snowflake, identity) and grouping methods (OCSF entity type and operator-facing groups). It also distinguishes itself from siblings by explicitly excluding findings.

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?

The description provides explicit guidance on when to use this tool versus alternatives: 'Findings (CVEs, misconfigurations) are excluded; use blast_radius or exposure_paths for the finding queue.' This clearly specifies the context and exclusion, helping the agent choose appropriately among sibling tools.

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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