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ddflow_remove

Removes an item from the work queue, logging the removal in the append-only history instead of erasing it. Preserves an honest record of dropped work and refuses when other items depend on it.

Instructions

Take an item out of the queue. The log is append-only, so this RECORDS a removal rather than erasing anything — the item stays in the history and in replay, which keeps the record honest about work that was planned and then dropped. Refuses if another item depends on it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesItem id.
forceNoRemove although it still has open children, or although other items depend on it. Both leave the queue inconsistent in a way the scheduler then reports, so read the refusal before overriding it.
reasonNoWhy.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the burden of disclosing behavior. It clearly states that the log is append-only, that the item remains in history and replay, and that removal is refused if another item depends on it. This is valuable non-obvious side-effect information, though it does not cover every consequence of using the force override.

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 three sentences and front-loads the core action before explaining the append-only behavior and dependency guard. The rationale about keeping the record honest is slightly verbose but earns its place by clarifying why the tool is non-destructive.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description covers the essential behavioral context: recording instead of erasing, retention in history/replay, and dependency refusal. The schema covers force and id details. The main gap is lack of sibling differentiation, but an agent has enough information to invoke the tool correctly.

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 baseline is 3. The description adds no parameter-specific meaning beyond what the schema already provides for id, force, and reason, so it neither gains nor loses points here.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Take an item out of the queue.' It further clarifies that this is a recorded removal, not an erasure, which gives the agent an accurate model of the operation. It does not explicitly distinguish itself from siblings like ddflow_abandon or ddflow_workflow_drop, so it loses the fifth point.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

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

The description implies the use case of recording planned work that was dropped, and it states a refusal condition when dependencies exist. However, it never names alternative tools or explains when to prefer another tool like ddflow_abandon or ddflow_workflow_drop, so usage guidance is only implied rather than explicit.

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