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Mipiti
by Mipiti

unreject_reconciliation_candidate

Revert a persisted reconciliation rejection, making the candidate eligible again for review in the active queue.

Instructions

Remove a persisted reconciliation rejection. Mutates state.

The pair becomes eligible to surface in the active candidate queue again on the next read of list_reconciliation_candidates. Use when the operator changes their mind about a prior rejection — the surrogate rejection_id comes from rejections[*].id on list_reconciliation_rejections (or the return value of reject_reconciliation_candidate).

Does NOT bump model version (rejection is org state, not model state).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYesID of the descendant threat model the rejection is on.
rejection_idYesSurrogate id of the persisted rejection.
server_versionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, so the description carries full burden. It states 'Mutates state', explains the effect on the candidate queue, and importantly notes that it does NOT bump model version, which is a useful behavioral trait. The description also clarifies data provenance for the rejection_id.

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 structured in three short paragraphs that are informative and front-loaded with the main purpose. While every sentence adds value, it could be slightly more concise, but it strikes a good balance between detail and brevity.

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 that an output schema exists (not shown but stated in context signals), the description does not need to explain return values. It adequately explains the effect (candidate becomes eligible), notes no model version bump, and provides necessary context for the rejection_id. This is complete for a mutation tool.

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 description coverage is 67% (2 of 3 parameters described). The description adds value for the rejection_id by explaining its origin from list_reconciliation_rejections or reject_reconciliation_candidate. For server_version, no additional info is provided, but overall the description compensates partially for the missing schema descriptions.

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 removes a persisted reconciliation rejection and makes the pair eligible again. It distinguishes itself from the sibling 'reject_reconciliation_candidate' by describing the reverse operation and specifying the source of the rejection_id.

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?

The description explicitly says to use when the operator changes their mind about a prior rejection. It provides context on where to get the rejection_id from (list_reconciliation_rejections or reject_reconciliation_candidate return value). It does not explicitly state when not to use, but the usage case is clear.

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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