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Mipiti
by Mipiti

list_reconciliation_candidates

Identify duplicate entities between a threat model and its ancestors. Surface reconciliation candidates with certain and heuristic tiers to collapse duplicates before they distort coverage.

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?

No annotations provided, so description carries full burden. It thoroughly discloses pagination behavior, the return shape with detailed fields, and the edge case when composition is disabled (total=0, candidates empty, flag_enabled=false). This exceeds the minimal requirement.

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?

Description is front-loaded with a clear purpose, followed by tier explanation, usage guidance, and a well-structured return shape. No redundant sentences; each part adds value.

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 a detailed output schema and input schema, the description covers purpose, usage, behavioral nuances, and edge cases (composition disabled). It is fully self-contained for an agent to correctly select and invoke the tool.

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% (3 of 4 parameters have descriptions). The tool description does not add meaning beyond what the schema provides; it only mentions pagination indirectly. Server_version parameter is undocumented in both schema and description. 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 verb 'list' and resource 'reconciliation candidates' between a model and its ancestors. The description explains the concept and distinguishes from related 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 Guidelines5/5

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

Explicitly says 'Use this on child models in a recursive tree to find duplicates that should be collapsed before they distort coverage.' Also explains the two tiers (certain vs heuristic) and their safety implications, guiding when to auto-apply vs review.

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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