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Mipiti
by Mipiti

get_composition_overview

Check composition status, tree metadata, and entity counts for a threat model to identify structural warnings before drilling into sub-resources.

Instructions

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

Cheapest call in the composition surface (~1-2KB). Use this first to learn whether composition is enabled, 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 TREE_COMPOSITION_ENABLED is off on the backend, returns the same shape with all counts zeroed and flag_enabled: false.

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?

Although no annotations are provided, the description fully discloses behavioral traits: it's a read-only, lightweight call (~1-2KB), returns a defined output shape, and handles the disabled case gracefully by returning zeroed counts and flag_enabled: false.

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 well-structured: a concise summary followed by a detailed code block for the return shape. Every sentence adds value, and it is front-loaded with the most important information. No wasted text.

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 complexity and the presence of an output schema, the description is complete. It explains the exact return shape, the meaning of each field, and edge cases (disabled feature). No critical information is missing for an AI agent to select and invoke the tool 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?

Schema description coverage is 50% (server_version lacks description). The tool description does not elaborate on the server_version parameter's purpose or constraints, nor does it add semantic meaning beyond the schema for model_id. With low coverage, the description should compensate but does not.

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 provides a 'composition index for a model — counts, tree metadata, warnings.' It distinguishes itself from sibling tools by specifying it's the cheapest call in the composition surface and lists specific outputs like entity counts, tree info, and warnings.

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?

Explicitly advises to 'Use this first' to learn about composition status, tree hierarchy, counts, and warnings before drilling deeper. Also describes behavior when the feature is disabled, providing clear guidance on when to use this tool.

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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