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decompose_plan

Break down a raw goal or feature request into structured epics/stories JSON. Returns the plan for review and saving, using your configured provider.

Instructions

Turn a raw goal/feature request into epics/stories JSON via the product-analyst persona, run on whichever provider the "decompose" role is configured for (PIPELINE_BACKEND_DECOMPOSE env var, or a "decompose" entry in model_registry.json - defaults to Claude when neither is set). This is a separate, additional path from the interactive product-analyst subagent (invoked via the Agent tool, which is always Claude) - that path remains available and is still the default choice for Claude-quality decomposition; this tool exists so decomposition can also run on a local provider when desired.

Does NOT call save_plan itself - review the returned plan the same way you would review the interactive subagent's output, then save_plan it yourself.

Returns {"ok": True, "plan": {...}} on success. On failure, returns {"ok": False, "error": ...}, with "raw": included whenever the backend actually returned text that failed to parse (never raises).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden and does so thoroughly. It discloses the success and failure return contracts, includes raw output on parse failures, states that it never raises, and clarifies that it does not persist the plan via save_plan.

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 longer than average, but every paragraph serves a distinct decision point: core action, provider configuration, relationship to the alternative path, save_plan responsibility, and return contract. The main purpose is front-loaded in the first sentence, and there is no obvious filler.

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 one parameter, no annotations, and the complexity of provider selection and output handling, the description is complete. It tells the agent exactly what to pass, what provider will be used, what the return shape is, what failure looks like, and what the agent must do after calling it.

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?

The schema has 0% description coverage, so the description must define the sole request parameter. It does this by calling it a 'raw goal/feature request', which is sufficient for a single free-form string parameter, though it does not give examples or length/format constraints.

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-resource pair: 'Turn a raw goal/feature request into epics/stories JSON'. It further distinguishes itself from the interactive product-analyst subagent and clarifies that it is a separate decomposition path, so an agent can tell it apart from siblings like save_plan and the Agent tool.

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 explains when to use this tool versus the interactive product-analyst subagent: the interactive path is still the default for Claude-quality decomposition, while this tool is for running decomposition on a local provider when desired. It also gives a clear behavioral instruction: do not expect it to call save_plan; review the output and save it yourself.

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