neurodock-mcp-task-fractionator 0.0.4

NeuroDock task fractionator MCP server — goal decomposition and next-action selection.

neurodock-mcp-task-fractionator

Local-first goal decomposition and single next-action selection, exposed as an MCP server.

• Version: v0.0.3 (developer preview)
• Status: Phase 1 substrate server.
• License: AGPL-3.0-or-later.
• Design: see ADR 0003 — task-fractionator tool design.
• Schemas (the contract): schemas/decompose.schema.json and schemas/next_one.schema.json.
• User stories driving this server: ("Director with ADHD" — voice-dump a vague initiative, get 5–12 atomic tasks plus the single next one).

Tools

decompose(goal, time_budget?)

Local-heuristic decomposition into atomic 5–90 minute tasks. Every task carries at least one acceptance criterion, a dependency-sorted sequence number, and a small bag of tags. Stateless: the server returns the list and does not persist it. Persistence is the caller's job via mcp-cognitive-graph.

• goal — 5..500 character plain-language goal. Never logged.
• time_budget — optional ISO 8601 duration (PT4H, P3D, PT2H30M). Unparseable input returns TIME_BUDGET_UNPARSEABLE. P3D is treated as 3 working blocks of 4 hours, not 72 hours, per ADR 0003 §3.

Errors: GOAL_REQUIRED, GOAL_TOO_LONG, TIME_BUDGET_UNPARSEABLE, BUDGET_INFEASIBLE, DEPENDENCY_CYCLE, ACCEPTANCE_CRITERIA_REQUIRED, DECOMPOSITION_UNAVAILABLE.

next_one(project)

Returns exactly one task — the single thing the user should do next — with a short reasoning paragraph and a confidence score in [0, 1].

• project — 1..120 char project name (case-sensitive, no fuzzy match).
• Pending tasks are read from a pluggable PendingTaskSource.
• Selection: lowest sequence whose dependencies are all already complete in the pending set.

Errors: PROJECT_REQUIRED, NO_TASKS_AVAILABLE, ALL_TASKS_BLOCKED, COGNITIVE_GRAPH_UNAVAILABLE.

Quick start

# From the repo root
uv sync
uv run pytest packages/mcp-task-fractionator/tests/ -v

# Run the server over stdio (the MCP transport):
uv run neurodock-mcp-task-fractionator

In-memory-only next_one (v0.0.3 caveat)

next_one reads pending tasks from a PendingTaskSource. v0.0.3 ships two implementations:

• InMemoryPendingTaskSource — default, used by tests. Holds tasks in process memory. Build the server explicitly with this source and seed it before calling next_one:

  from neurodock_mcp_task_fractionator import (
      InMemoryPendingTaskSource, build_server,
  )
  
  source = InMemoryPendingTaskSource()
  source.add("founding-scope-rfc", [task_1, task_2, task_3])
  server = build_server(source=source)
  

• CognitiveGraphPendingTaskSource — Phase 2 stub. Always raises COGNITIVE_GRAPH_UNAVAILABLE. Real wiring lands once the mcp-cognitive-graph Python client ships.

Selection via env var: NEURODOCK_TASK_SOURCE=memory|graph (default memory). Unknown values fall back to memory rather than crashing.

Privacy

Per ADR 0003 §7 the goal text and project name are treated as sensitive user data:

• No remote calls. No LLM call inside the server.
• Goal text is never logged at any level.
• Validation errors reference field names, not user content.
• The rationale paragraph is built from recognised vocabulary tokens only; it never embeds the user's original goal text verbatim.

A test (test_decompose.py::test_rationale_does_not_contain_goal_text) encodes the privacy invariant.

Development gates

# Lint + format check
uv run ruff check packages/mcp-task-fractionator/
uv run ruff format --check packages/mcp-task-fractionator/

# Strict type check
uv run mypy --strict packages/mcp-task-fractionator/src/

# Tests
uv run pytest packages/mcp-task-fractionator/tests/ -v

All four must pass before a PR is mergeable.

Pointers

• Architecture rationale: ADR 0003 — task-fractionator tool design.
• The five-principles manifesto this server has to honour: MANIFESTO.md.
• Sibling servers: mcp-chronometric (time, idle, breaks) and mcp-cognitive-graph (persistent state — where pending tasks will live).