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withdraw_proposal

Remove all still-pending shapes from a takeoff proposal batch in one journal step. Use it to cancel a wrong batch while keeping accepted shapes and a withdrawable record.

Instructions

Take a proposal back (#365): every still-pending shape in the batch is removed in ONE journal step, the record stays marked withdrawn (its label is history the estimator may still read), and new commits stop attaching to it. Shapes the estimator already accepted are ink and stay — the reply counts them. This is the honest exit for "that batch was wrong" — one call instead of N delete_shape calls, and undo_last restores the whole batch.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
proposal_idYesThe batch, from propose_takeoff

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteYes
labelYes
withdrawnYesPending shapes removed
proposal_idYes
accepted_keptYesShapes from the batch the estimator had accepted — untouched

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.21

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral burden and does so well. It discloses atomic journaling ('ONE journal step'), what is removed (still-pending shapes), what persists (the record stays marked withdrawn, accepted shapes stay), what happens to future commits (new commits stop attaching), and reversibility via undo_last.

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?

The description is three sentences, front-loaded with the core action and followed by relevant behavioral and usage details. Every sentence earns its place by clarifying scope, effect, or alternatives, without padding.

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?

For a mutation tool with no annotations but an output schema, the description gives the agent everything needed to call it correctly: the batch parameter, what gets removed, what remains, the atomicity guarantee, and how it relates to delete_shape and undo_last.

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 100%, so the parameter's meaning is already fully documented. The description reinforces the concept of a batch/proposal but does not add syntax, format, or constraints beyond what the schema provides, so the baseline of 3 applies.

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 states a specific verb ('Take a proposal back') and resource (proposal/batch), and clearly scopes the action to pending shapes in the batch. It distinguishes the operation from delete_shape by noting that one call replaces N delete_shape calls, and from undo_last by noting that undo_last restores the whole batch.

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?

It explicitly identifies the situation for use ('that batch was wrong') and names alternatives: one call instead of N delete_shape calls, and undo_last restores the whole batch. This gives the agent enough to choose this tool over deleting individual shapes or reversing work.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.