Skip to main content
Glama
Mipiti

Mipiti MCP Server

by Mipiti
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Get Composition Overview

get_composition_overview

Retrieve a threat model's composition overview to verify availability and inspect its tree position, entity counts, and structural warnings 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

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.62.2
  2. Removedv0.62.2
  3. First observedv0.57.0

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and discharges it thoroughly: it declares 'Read-only; no side effects', gives cost/size expectations (~1-2KB, cheapest), and discloses the critical edge case — when composition is unavailable the call returns zeroed counts with flag_enabled false rather than throwing, so an agent won't mis-handle it as an error. It also explains the tree/counts/warnings semantics a caller needs.

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 long but every part earns its place: a one-line summary, a usage/context paragraph, a structured return-shape block, and a edge-case caveat. The essential summary is front-loaded and the special behavior is last, making it easy to scan despite the length.

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?

An output schema covers the return shape, and the description adds the behavioral and usage context around it, including the unavailable-backend case. The only material gap is the meaning of server_version, which is required but left undocumented both in the schema and the description; otherwise an agent has everything needed to invoke this tool correctly.

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 schema covers 50% of parameters: model_id has a description, but server_version has none, and the tool description does not clarify what server_version is or how it affects the call. The description adds context about the output being keyed by model_id and model_version, but it does not compensate for the undocumented required parameter, so the score stays at baseline.

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 opening line — 'Composition index for a model — counts, tree metadata, warnings' — names a specific verb and resource and states exactly what is returned. The description further distinguishes this from the many sibling tools by positioning it as the 'cheapest call in the composition surface' and the one to 'use it first', setting it apart from the other composition, coverage, and entity tools. The purpose is unambiguous.

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

Usage Guidelines4/5

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

'Use it first to learn whether composition is available for this model' is an explicit when-to-use directive, and 'before drilling into sub-resources' implies the alternative path of calling more specific tools. It does not name particular sibling alternatives or state when not to use it, so it falls short of a 5, but it gives clear context well above the minimum.

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

Deploy Server

Other Tools