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Mipiti
by Mipiti

apply_certain_reconciliation_match

Apply a certain-tier reconciliation candidate to resolve duplicate entities in threat models. This soft-deletes the descendant's own duplicate and promotes the inherited entity as canonical.

Instructions

Apply a certain-tier reconciliation candidate. Mutates state.

Soft-deletes the descendant's own duplicate entity; the inherited entity becomes the canonical surface for the effective-model resolver. Use after surveying candidates via list_reconciliation_candidates. Certain-tier candidates apply directly; heuristic-tier candidates need operator review of the structural divergence and are refused server-side unless confirm_heuristic=True is passed to acknowledge the divergence.

The server re-validates the candidate against current live state before applying; if the model has moved since the candidate was detected, returns 400 and the operator should refresh the candidate list and retry. Bumps model version and emits an activity event on success.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYesEntity kind — one of ``"assets"``, ``"attackers"``, ``"components"``.
own_qidYesQualified id of the descendant's own duplicate (e.g. ``"child:A1"``).
model_idYesID of the descendant threat model the duplicate is on.
inherited_qidYesQualified id of the canonical entity on the ancestor (e.g. ``"parent:A1"``).
server_versionYes
confirm_heuristicNoAcknowledge and apply a heuristic-tier candidate despite its structural divergence. Default False — heuristic-tier matches are refused server-side without this flag. Leave False for certain-tier candidates.

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 carries the burden. It discloses that the tool mutates state, soft-deletes the descendant's duplicate, bumps the model version, emits an activity event, and re-validates the candidate against live state (returning 400 if stale). It also explains the reject behavior for heuristic-tier without the confirm flag.

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?

The description is a single paragraph but well-organized: main action first, then details on behavior, usage flow, and error handling. It is concise without being terse, though could benefit from bullet points for structured readability.

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 (6 params, no annotations, has output schema), the description covers the full usage context: when to use, what it does, side effects, error conditions, and parameter guidance. It is sufficient for an AI agent to invoke the tool correctly.

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

Parameters4/5

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

Schema coverage is 83%, so the schema already describes most parameters. The description adds value by elaborating on the meaning of 'confirm_heuristic' and how it relates to candidate tier, which goes beyond the schema's default description. No other parameters need clarification.

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 applies a certain-tier reconciliation candidate and mutates state. It explicitly names the action (apply), the resource (reconciliation candidate), and distinguishes it from related tools like list_reconciliation_candidates and reject_reconciliation_candidate by explaining the soft-deletion and inheritance behavior.

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?

The description provides explicit guidance: use after surveying candidates via list_reconciliation_candidates, and distinguishes between certain-tier (direct apply) and heuristic-tier (requires confirm_heuristic=True). It also warns that the server re-validates and returns 400 on stale state, advising to refresh and retry.

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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