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Mipiti
by Mipiti

get_composition_overview

Retrieve composition overview for a threat model, including tree metadata, entity counts, and warnings to assess availability and structure before drilling into sub-resources.

Instructions

Composition index for a model — counts, tree metadata, warnings.

Read-only; no side effects. Cheapest call in the composition surface (~1-2KB). Use it first to learn whether composition is available for this model, where the model sits on the recursive tree (parent + ancestor chain + child ids), how many own vs inherited entities and COs there are per kind, and whether any structural warnings (cycle, parent missing, max depth exceeded) need surfacing before drilling into sub-resources.

Return shape::

{
  model_id, model_version, flag_enabled,
  tree: {parent_id, ancestor_chain, depth, child_ids},
  counts: {
    entities: {kind: {own, inherited}, ...},
    control_objectives: {total, live, covered, uncovered,
      indeterminate, by_origin: {own, cross, inherited}},
    reconciliation_candidates: {certain, heuristic},
  },
  warnings: [str, ...],
}

When composition is not available on the backend, the same shape is returned with all counts zeroed and flag_enabled: false — detect that rather than handling an error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYesID of the threat model.
server_versionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

The description discloses that the tool is read-only with no side effects, states an approximate response size (~1-2KB), and details the exact return shape. It also explains the fallback behavior when composition is unavailable (same shape with zeroed counts and flag_enabled: false), which is critical non-error behavior not inferable from annotations (none provided).

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 front-loaded with a clear definition, then provides purposeful usage guidance, return shape, and edge-case behavior. Every sentence adds value; the length is justified by the complexity of the returned data structure.

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?

Despite the presence of an output schema (not shown), the description includes a comprehensive return shape and behavioral details, covering normal operation, unavailability fallback, warning types, and the intended usage sequence relative to sibling tools. It is complete for an agent to select and invoke correctly.

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

Parameters2/5

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

The input schema has 50% description coverage: model_id is described as 'ID of the threat model,' but server_version has no description. The tool description adds no additional meaning for either parameter and does not explain the role or format of server_version. The detailed return shape does not compensate for the missing parameter semantics.

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 opens with 'Composition index for a model — counts, tree metadata, warnings,' which clearly states what the tool does. It further specifies the resource (a threat model) and distinguishes itself from sibling tools by calling itself the cheapest call in the composition surface, meant to be used first.

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?

Explicit guidance is provided: 'Use it first to learn whether composition is available for this model... before drilling into sub-resources.' It explains when to use this tool (before detailed composition operations) and what to check (warnings, tree structure, counts), giving clear context for selection.

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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