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Deep Planning

deep_planning

Manages multi-phase planning sessions by moving through init, clarify, explore, evaluate, and finalize phases, tracking reasoning state for structured problem-solving.

Instructions

A structured planning tool that manages multi-phase planning sessions. Complements sequential_thinking by tracking planning state while the LLM reasons deeply.

Workflow: init → clarify → explore → evaluate → finalize

  • init: Define the problem, context, and constraints

  • clarify: Record clarifying questions and answers (repeatable)

  • explore: Record approach branches with pros/cons (repeatable)

  • evaluate: Score approaches on feasibility, completeness, coherence, risk (repeatable)

  • finalize: Select best approach and generate structured implementation plan

Each phase returns valid next phases to guide the workflow. Complex fields (pros, cons, steps, risks, constraints) are passed as JSON strings.

Use sequential_thinking for deep reasoning between phases. Use deep_planning to record conclusions and track planning state.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
consNoJSON array of disadvantage strings
nameNoShort approach name (required for explore)
prosNoJSON array of advantage strings
riskNoRisk score 0-10 (lower is better)
phaseYesCurrent planning phase
risksNoJSON array of risk objects with description and mitigation
stepsNoJSON array of implementation step objects
answerNoAnswer to the clarifying question
formatNoOutput format: markdown (default) or json
contextNoAdditional background context
problemNoProblem statement (required for init)
branchIdNoUnique approach identifier (required for explore/evaluate)
planNameNoDescriptive plan name (init phase only). Sanitized to kebab-case. Generates dp-YYYYMMDD-{name} session ID.
questionNoClarifying question (required for clarify)
coherenceNoCoherence score 0-10
rationaleNoReasoning for evaluation scores
sessionIdNoResume a specific session by ID. Required when switching between multiple sessions. Ignored on init phase.
assumptionsNoJSON array of assumption strings
constraintsNoJSON array of constraint strings
descriptionNoDetailed approach description
feasibilityNoFeasibility score 0-10
completenessNoCompleteness score 0-10
recommendationNopursue, refine, or abandon
selectedBranchNoBranch ID of chosen approach (required for finalize)
successCriteriaNoJSON array of success criteria strings
Behavior4/5

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

No annotations are provided, so the description bears full responsibility. It discloses that complex fields are JSON strings and that each phase returns valid next phases. It also mentions session resumption via sessionId and ID generation from planName. However, it does not explicitly state side effects, idempotency, or auth requirements, which are minor omissions.

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 long but well-structured: a summary sentence, a workflow list, and usage clarifications. Each sentence adds value, though it could be slightly more concise without losing meaning.

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?

Given the tool has 25 parameters and no output schema, the description explains the workflow and parameter usage well but falls short on return values. It only mentions that phases return valid next phases, lacking detail on the full output structure. This is a gap for an AI agent.

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 coverage is 100%, so the baseline is 3. The description adds value by explaining which parameters are relevant per phase (e.g., 'name required for explore', 'problem required for init') and that complex fields are JSON strings, which is beyond the 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 it is a 'structured planning tool that manages multi-phase planning sessions' and distinguishes itself from the sibling tool 'sequential_thinking' by noting it 'complements sequential_thinking' and 'tracks planning state'. The workflow phases are explicitly listed, providing a specific verb-resource-action.

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 advises when to use this tool vs. the sibling: 'Use sequential_thinking for deep reasoning between phases. Use deep_planning to record conclusions and track planning state.' It also outlines the workflow, making it clear how to proceed across phases.

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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