New in v0.14

MCP Server

Give Claude direct access to your decision graph. 31 tools for searching, tracing, and analyzing decisions. Works with Claude Code and Claude Desktop.

Quick Start: Cowork Coming soon

MCP support for Claude Cowork is not yet available — cowork agents currently can't load custom MCP servers. Track progress on anthropics/claude-code#48909.

In the meantime, use the Claude Code setup below for project-level work, or Claude Desktop for free-form querying.

Quick Start: Claude Desktop

~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "deciduous": {
      "command": "/Users/you/.cargo/bin/deciduous",
      "args": ["mcp"],
      "env": {
        "DECIDUOUS_DB_PATH": "/path/to/your/project/.deciduous/deciduous.db"
      }
    }
  }
}

Use the absolute path to the deciduous binary (run which deciduous to find it) — Claude Desktop doesn't inherit your shell PATH. Point DECIDUOUS_DB_PATH at the SQLite database in your project's .deciduous/ directory, then restart Claude Desktop.

Quick Start: Claude Code

Setting up MCP in Claude Code
Config
Add to .claude/settings.local.json:
{ "mcpServers": { "deciduous": { "command": "deciduous", "args": ["mcp"] } } }
Result
Restart Claude Code. Tools appear as mcp__deciduous__*
You
What decisions were made about authentication?
Claude
search_nodes({ query: "auth" })
Found 8 nodes matching "auth"
Claude
trace_chain({ node_id: 5, direction: "outgoing" })
{ node_count: 12, edge_count: 11 }
Claude
The auth system went through 2 pivots. Started with JWT everywhere (Goal #5), hit mobile cookie size limits (Observation #8), pivoted to hybrid approach (Revisit #9, Decision #10). 12 nodes trace the full story.

Also available: /query slash command wraps MCP tools into a natural language interface.

Natural Language Queries

In Claude Code, /query uses MCP tools to answer questions about your decisions:

Claude Code — /query in action
You
/query why did we choose an embedded MCP server over a CLI wrapper?
Claude
Querying decision graph...
search_nodes({ query: "embedded MCP" }) → 2 matches get_node_context({ node_id: 1361 }) → 3 parents, 7 children trace_chain({ node_id: 1361 }) → 13 nodes, 14 edges

Why Embedded Over CLI Wrapper

The embedded approach won at 92% confidence. It fits the project's "functional core, imperative shell" constraint and gives direct DB access with no subprocess overhead.

Options Compared

OptionConfidenceVerdictRationale
#1358 Embedded server90%ChosenSingle binary, pure functions, testable
#1359 CLI wrapper40%RejectedProcess spawn per call, fragile text parsing
#1360 Separate crate50%RejectedUnnecessary build complexity

Decision Chain

Goal #1356 — Add MCP server to deciduous
Option #1358 — Embedded MCP server chosen
Option #1359 — Thin CLI wrapper rejected
Option #1360 — Separate crate rejected
Decision #1361 — Choose embedded MCP server [92%]
Action #1362 — Implement protocol types
Action #1363 — Build tool registry
Action #1364 — Implement CRUD handlers
Action #1365 — Build query/analysis engine
Action #1366 — Wire server loop
Action #1367 — Write test suite

Key Observation

#1357: “User explicitly requested functional core, imperative shell, lots of tests” — this made pure-function parsing and zero-IO modules the obvious architectural choice.
7 actions completed. 108 MCP-specific tests passing. All pure functions trivially unit-testable, as the architecture predicted.

Searching Decisions

Real output from deciduous's own graph (1,300+ nodes from building this tool):

search_nodes — full-text across titles, descriptions, prompts
You
What decisions were made about the MCP server?
Claude
search_nodes({ query: "MCP" })
{ "count": 26, "nodes": [ { "id": 1356, "node_type": "goal", "title": "Add MCP server to deciduous", "confidence": 90 }, { "id": 1361, "node_type": "decision", "title": "Choose embedded MCP server (Option A)", "confidence": 92 }, { "id": 1381, "node_type": "outcome", "title": "Session lifecycle complete", "confidence": 95 }, ... ] }

Tracing Decision Chains

BFS traversal from any node. Shows the full connected decision tree with depth:

trace_chain — follow edges outward from a goal
You
Show me everything that came from the MCP server goal
Claude
trace_chain({ node_id: 1356, direction: "outgoing", max_depth: 2 })
{ "start_node_id": 1356, "node_count": 13, "edge_count": 14, "nodes": [ { "id": 1356, "node_type": "goal", "title": "Add MCP server to deciduous", "depth": 0 }, { "id": 1358, "node_type": "option", "title": "Embedded MCP server via deciduous mcp subcommand","depth": 1 }, { "id": 1359, "node_type": "option", "title": "Thin CLI wrapper - shells out to commands", "depth": 1 }, { "id": 1360, "node_type": "option", "title": "Separate mcp-deciduous crate/binary", "depth": 1 }, { "id": 1361, "node_type": "decision", "title": "Choose embedded MCP server (Option A)", "depth": 2 }, { "id": 1376, "node_type": "goal", "title": "Auto-create decision trees per conversation", "depth": 1 }, { "id": 1377, "node_type": "option", "title": "Session-scoped: start/end_session tools", "depth": 2 } ] }
Claude
The MCP goal spawned 3 options at depth 1 (embedded, CLI wrapper, separate crate). The embedded approach was chosen (depth 2). A sub-goal for conversation scoping also branched off at depth 1.

Node Context

Get a decision's full neighborhood — what options led to it, what actions came from it:

get_node_context — parents, children, siblings
You
What led to choosing the embedded MCP server, and what came from it?
Claude
get_node_context({ node_id: 1361 })
Node: #1361 [decision] "Choose embedded MCP server (Option A)" (92%) Parents (3 options considered): #1358 [option] "Embedded MCP server via deciduous mcp subcommand" (90%) #1359 [option] "Thin CLI wrapper MCP - shells out to commands" (40%) #1360 [option] "Separate mcp-deciduous crate/binary" (50%) Children (7 actions spawned): #1362 [action] "Implement MCP protocol types (protocol.rs)" #1363 [action] "Build tool registry and definitions (tools.rs)" #1364 [action] "Implement CRUD handlers (handlers.rs)" #1365 [action] "Build query/analysis engine (query.rs)" #1366 [action] "Wire server loop and CLI subcommand" #1367 [action] "Write comprehensive test suite" #1374 [action] "Create /query skill for NL reports"
Claude
3 options were considered. The embedded approach won at 92% confidence (CLI wrapper was only 40% — too fragile). It spawned 7 implementation actions covering protocol, tools, handlers, query engine, server loop, tests, and a slash command.

Graph Pulse

Health check — stats, active goals, and what needs attention:

get_pulse — branch health at a glance
Claude
get_pulse({ branch: "add-mcp-server" })
total_nodes: 37 | total_edges: 1229 By type: goal: 4 | option: 6 | decision: 3 action: 11 | outcome: 11 | observation: 2 Active goals: #1356 Add MCP server to deciduous #1376 MCP server auto-creates decision trees for each conversation #1387 Write MCP setup docs for README + deciduous.dev #1391 Rewrite MCP docs: quickstarts first, real examples

Sessions: Per-Conversation Trees

Each conversation gets its own scoped decision tree. Nodes auto-associate.

Cowork agent starting a session
Claude
start_session({ name: "cowork: add MCP server", goal_title: "Add MCP server to deciduous" })
{ "session_id": 1, "root_node_id": 1356, "name": "cowork: add MCP server" }
Later
... hours of work, server may restart ...
Claude
get_session({})
{ "session_id": 1, "is_active": true, "node_count": 37, "nodes": [...] }
After a break
Explicitly reconnect to a previous session:
Claude
resume_session({ session_id: 1 })
{ "session_id": 1, "node_count": 37, "message": "Resumed session #1 (37 nodes)" }

Sessions persist to .deciduous/active_session. Server restarts auto-resume. Long pauses use resume_session.

MCP API Reference →

All 31 tools with parameters, types, and CLI equivalents.