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derive_features

Generate a proposed feature checklist from documentation and existing features, then add it to the backlog for approval, skipping any candidates that already exist.

Instructions

Derive a feature checklist for a goal from the documentation and existing features, and put it straight in the backlog at 'proposed' — recorded, not yet agreed. Defining a goal must produce work, not just an objective. Call project_boundaries FIRST: re-proposing something already refused wastes a review round and teaches the principal that the queue does not learn. Candidates matching an existing feature are reported rather than created.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cwdNoDirectory identifying the project and the tree. Defaults to this session's working directory, which in a git worktree is that worktree.
goalYes
noteNo
actorNo
sourceNoWhat was read to derive these.
candidatesYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden, and it discloses key side effects: records are placed in the backlog at 'proposed', not agreed, and candidates that match existing features are reported rather than created. It also exposes the prerequisite of calling project_boundaries first, which is valuable behavioral context.

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 tight and front-loaded: the core action is stated first, followed by the status implication, the rationale, and the required ordering with project_boundaries. Each sentence contributes either purpose, workflow, or behavior, with no wasted filler.

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

Completeness3/5

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

The description covers the tool's role, side effects, deduplication behavior, and a prerequisite call, which is strong for a mutation tool with no annotations. However, it does not describe the return/value produced after invocation, and it leaves several parameters under-explained despite low schema coverage. It is adequate but has clear gaps.

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

Parameters2/5

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

Schema description coverage is only 33%, so the description must compensate for undocumented parameters. It mentions 'goal' and 'candidates' semantically and explains matching behavior, but it does not explain parameter meanings, how candidates should be structured, or what 'source' and 'cwd' contribute. The description adds little beyond the property names.

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 opens with a specific verb and resource: 'Derive a feature checklist for a goal from the documentation and existing features' and clarifies the exact disposition 'put it straight in the backlog at proposed'. It also distinguishes itself by explaining that matching candidates are 'reported rather than created', so an agent can separate it from feature_propose and similar siblings.

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 gives clear workflow context: call project_boundaries FIRST to avoid re-proposing refused work, and it frames derivation as a required output of goal definition ('Defining a goal must produce work'). However, it does not explicitly name when-not-to-use it or contrast it with alternative derive_* or feature_* tools.

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