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Mipiti
by Mipiti

reject_reconciliation_candidate

Reject a reconciliation candidate to confirm a pair of entities is not a duplicate, removing it from future candidate lists and reducing false positives.

Instructions

Reject a reconciliation candidate. Mutates state.

Records the operator's "these are NOT duplicates" decision at org scope so the candidate detector filters this pair out of the active queue on subsequent reads. Idempotent on the natural key (model_id, kind, own_qid, inherited_qid) — re-rejecting an existing pair returns the same row. Use when list_reconciliation_candidates surfaces a pair that looks like a duplicate but the operator has confirmed it is not.

Persistence is at org scope, not model state — the rejection is durable across sessions and teammates but does NOT bump model version.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYesEntity kind — one of ``"assets"``, ``"attackers"``, ``"components"``.
own_qidYesQualified id of the descendant's own entity (e.g. ``"child:A1"``).
model_idYesID of the descendant threat model.
inherited_qidYesQualified id of the ancestor's entity (e.g. ``"parent:A1"``).
server_versionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries full burden. It discloses mutation, idempotency, org-scope persistence, and that it does not bump model version. It lacks details on authorization or potential errors, but covers key behavioral aspects well.

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 concise and front-loaded with the core action and key details. It uses clear language but could be slightly more structured (e.g., bullet points for idempotency and persistence). Still efficient overall.

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 (5 params, output schema exists, many siblings) and no annotations, the description covers purpose, usage, behavioral details, and idempotency. The existence of an output schema reduces the need to explain return values, making the description complete.

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 description coverage is 80%, with most parameters well-described. The description adds minimal new info beyond the schema, notably missing details on the 'server_version' parameter (the 20% uncovered). 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?

The description clearly states the verb 'reject' and the resource 'reconciliation candidate'. It distinguishes from siblings like 'list_reconciliation_candidates' and 'unreject_reconciliation_candidate' by noting its effect on state and its use case.

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 explicitly states when to use the tool: 'Use when list_reconciliation_candidates surfaces a pair that looks like a duplicate but the operator has confirmed it is not.' It also provides context on idempotency and persistence, guiding proper usage.

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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