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Mipiti
by Mipiti

list_reconciliation_candidates

Find reconciliation candidates between a threat model and its ancestors to alias duplicate entities and prevent coverage distortion.

Instructions

Reconciliation candidates between this model and its ancestors.

When a model inherits entities (assets, attackers, components, trust boundaries) from an ancestor and the operator has authored a locally-named entity that looks like the same real-world thing, the reconciliation engine surfaces the pair as a candidate so the operator can decide whether to alias it onto the inherited qualified id. Tier certain is a deterministic match (same qid or structurally identical) and is safe to auto-apply; tier heuristic is a fuzzy name/description match that needs review.

Paginated. Use this on child models in a recursive tree to find duplicates that should be collapsed before they distort coverage.

Return shape::

{
  model_id, flag_enabled, total,
  tiers: {certain: int, heuristic: int},
  page, page_size,
  candidates: [
    {kind, own_qid, inherited_qid,
     tier: "certain"|"heuristic", reasons: [str, ...]},
    ...
  ],
}

When composition is disabled on the backend, total is 0, candidates is empty, and flag_enabled: false.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo1-indexed page number. Default 1.
model_idYesID of the threat model.
page_sizeNoItems per page. Default 50.
server_versionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description fully discloses behavior: pagination, auto-safe certain tier, composition-disabled edge case (total=0, candidates empty, flag_enabled false). No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with a clear output example and front-loaded purpose. Slightly verbose but each sentence adds value, no wasted words.

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 complexity (reconciliation concept, pagination, tiers, edge cases), the description covers usage context, return shape, and practical scenarios. Output schema is provided, so return values are fully defined.

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?

Schema coverage is 75% with three of four parameters documented. The description adds little beyond the schema for parameters, focusing instead on behavioral details. 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?

Clearly states the tool lists reconciliation candidates between a model and its ancestors. Explains the context of entity inheritance and aliasing, and distinguishes from sibling tools like apply_certain_reconciliation_match and reject_reconciliation_candidate.

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?

Explicitly describes when to use: on child models to find duplicates that should be collapsed. Provides context about the tiers and pagination. Lacks explicit when-not-to-use, but the sibling tools cover related operations.

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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