PluginBench
MCP Server
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MIT

State Memory MCP MCP Server

io.github.putervision/state-memory-mcp

Deterministic, persistent SQLite graph for AI agents to track workflow state, decisions, and blockers with zero infrastructure.

What is the State Memory MCP MCP server?

State Memory MCP is a Model Context Protocol server that provides AI coding assistants with a structured, persistent SQLite graph for tracking workflow state—tasks, decisions, artifacts, plans, and blockers with their semantic relationships. It eliminates multi-step file scanning, reduces latency by 67–74%, and keeps all state local and private within your workspace.

State Memory MCP gives AI agents like Claude and Cursor a deterministic memory system for complex workflows. Instead of re-scanning files or losing context, agents can query a local SQLite database to retrieve unblocked tasks, trace dependencies, verify specs, and log decisions in milliseconds. It's designed for multi-turn coding sessions, spec-driven development, and parallel multi-agent coordination.

How to install State Memory MCP

Copy-paste configuration for popular MCP clients.

transport: stdio
Config generated by PluginBench — verify against the source before use.
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "state-memory-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@putervision/state-memory-mcp"
      ]
    }
  }
}

Tools & capabilities

Tools this server exposes to the agent.

  • manage_nodes — Node CRUD, FTS5/TF-IDF vector search, atomic batch mutations, observation notes, and thresholded fast decision logging for tasks, decisions, blockers, and other node types.
  • manage_edges — Typed DAG links and multimodal visual state linking between nodes with cycle detection.
  • manage_tasks — Topological dependency queue, blocker detection, task completion with artifacts, and auto-pruning.
  • manage_sessions — Agent attribution, turn tracking, and context bootstrap for multi-agent coordination.
  • manage_specs — PRD/RFC parsing, requirement-to-task decomposition, live acceptance criteria verification, and compliance scoring.
  • get_analytics — Velocity metrics, burndown charts, token ROI, cognitive load, and critical path analysis.
  • get_events — SHA-256 tamper-evident append-only event ledger for audit trails.
  • run_diagnostics — DAG validation, health checks, and AST reference integrity verification.
  • manage_snapshots — Checkpoints and time-travel undo for state rollback.
  • manage_database — Backups, checksum audits, and VCS branch merge operations.
  • manage_data — Bulk import/export, ML trajectories with interleaved fast decision events.
  • query_graph — Subgraph queries, dependency tracing, raw SQL, and sub-1KB compact task slices.
  • use_blackboard — Multi-agent asynchronous topic board for parallel subagents to publish decisions and blockers.

Use cases

  • Track multi-turn coding workflows with persistent task queues, blockers, and decision history without re-scanning files.
  • Decompose specifications (PRDs, RFCs) into tasks and verify compliance in real time as an agent works.
  • Enable parallel multi-agent coordination via a shared blackboard for safe, asynchronous task and blocker updates.
  • Analyze agent velocity, cognitive load, token ROI, and critical path to optimize workflow efficiency.
  • Time-travel undo and snapshot checkpoints to roll back state or branch workflows without losing history.

State Memory MCP MCP server FAQ

What is State Memory MCP?

State Memory MCP is a zero-infrastructure MCP server that gives AI coding assistants a deterministic, persistent SQLite graph for tracking workflow state—tasks, decisions, blockers, and their relationships. It eliminates context loss and file re-scanning, reducing latency by 67–74%.

Is it free?

Yes. State Memory MCP is released under the MIT License and is free to use. All state stays local in your workspace; no telemetry or data transmission occurs.

How do I install it in Cursor or Claude?

Install globally via npm: `npm install -g @putervision/state-memory-mcp`, then run `state-memory-mcp init` in your project directory. This creates `.state-memory-mcp/`, updates `.gitignore`, and auto-configures your IDE (Cursor, VS Code, Claude, Windsurf, etc.). Restart your IDE to activate.

Does it require authentication or external services?

No. State Memory MCP is 100% local and private. It uses a local SQLite database in `.state-memory-mcp/` and requires no external APIs, authentication, or cloud services.

Can multiple agents use the same state graph?

Yes. The `use_blackboard` tool enables multi-agent coordination via an asynchronous topic board, allowing parallel subagents to safely publish decisions, tasks, and blocker updates to a shared context store.

What are the system requirements?

Node.js >= 18.18.0 is required. The server is compatible with Cursor, Claude Code, Gemini, Copilot, VS Code, Windsurf, and other AI coding assistants that support MCP.

README (reference)

Source of truth, from the repository.

@putervision/state-memory-mcp

npm version version npm downloads CI Node TypeScript Website License: MIT

@putervision/state-memory-mcp is a zero-infrastructure, deterministic Model Context Protocol (MCP) server that provides AI coding assistants (such as Cursor, Claude Code, Gemini, or Copilot) with a structured, persistent SQLite graph for tracking workflow state—tasks, decisions, artifacts, plans, blockers, and their semantic relationships.

🌐 Official Documentation & Website: statememorymcp.com


⚡ Quick Start & Installation

Prerequisites: Node.js >= 18.18.0

# 1. Install globally
npm install -g @putervision/state-memory-mcp

# 2. Navigate to your project directory
cd your-project

# 3. Initialize state-memory-mcp
# Creates .state-memory-mcp/, updates .gitignore, registers project,
# and scaffolds IDE instructions and MCP configs for Cursor, Claude, VS Code, Windsurf, etc.
state-memory-mcp init

# Done! Restart your IDE or Agent Manager to activate.

Alternative Options

# Run directly via binary (after global install)
state-memory-mcp run

# Re-initialize across all registered workspace projects
state-memory-mcp init-global

🌟 Key Highlights

  • 🧠 Deterministic State Memory & Compact TaskSlices: Zero LLM in the loop for memory operations; fast, deterministic SQLite graph traversals, and sub-1KB TaskSlice extraction for System 1 fast path evaluation.
  • ⚡ 13 Production-Grade Consolidated MCP Tools: Full CRUD, relationship linking, DAG cycle checks, FTS5 search, TF-IDF RAG, time-travel history rollback, Spec-Driven Development, and auto-healing validation.
  • 📉 Efficient Context Management & Decision Thresholding: Offloads context to a local SQLite database, filters sub-0.70 routine decisions to append-only event logs to prevent graph bloat, and preserves high-significance turns.
  • 🚀 67%–74% Latency Reduction: Eliminates multi-step file scanning loops; agents retrieve unblocked tasks and blockers in milliseconds.
  • 🤝 Multi-Agent Blackboard: Shared Context Store allowing parallel subagents to publish decisions, tasks, and blocker updates safely.
  • 🎨 Interactive 3D Visualizer: Browser-based dark-mode 3D WebGL force-directed graph visualizer (state-memory-mcp view).
  • 🔗 Dual-MCP Synergy: Pair with @putervision/vision-memory-mcp for visual state caching, perceptual hashing, and cryptographic multimodal evidence packs.
  • 🛡️ 100% Local & Private: Local-first architecture; all state stays inside .state-memory-mcp/ in your workspace.

🛠️ MCP Tool Suite

@putervision/state-memory-mcp provides 13 production-grade consolidated MCP tools organized across 5 core workflow domains:

  • Graph & Relationships: manage_nodes (node CRUD, FTS5/TF-IDF vector search, atomic batch mutations, observation notes, thresholded fast decision logging), manage_edges (typed DAG links, multimodal visual state linking).
  • Task Execution & Work Queue: manage_tasks (topological dependency queue, blocker detection, task completion with artifacts, auto-prune), manage_sessions (agent attribution, turn tracking, context bootstrap).
  • Spec-Driven Development (SDD): manage_specs (PRD/RFC parsing, requirement-to-task decomposition, live acceptance criteria verification, compliance scoring).
  • Analytics, Audit & Diagnostics: get_analytics (velocity, burndown, token ROI, cognitive load, critical path), get_events (SHA-256 tamper-evident event ledger), run_diagnostics (DAG validation, health checks, AST reference integrity).
  • Data, Snapshots & Multi-Agent: manage_snapshots (checkpoints, time-travel undo), manage_database (backups, checksum audits, VCS branch merge), manage_data (bulk import/export, ML trajectories with interleaved fast decision events), query_graph (subgraphs, dependency tracing, raw SQL, sub-1KB compact_slice), use_blackboard (multi-agent asynchronous topic board).

👉 For complete parameter specifications, return schemas, and example payloads, see the Tools Reference Guide and Formal API Reference.


🚀 Architecture & State Graph Lifecycle

                      AI Agent Prompt / Task
                                │
                                ▼
               ┌─────────────────────────────────┐
               │  Agent Session Attribution       │ ──▶ manage_sessions(action: "start")
               └────────────────┬────────────────┘
                                │
                                ▼
               ┌─────────────────────────────────┐
               │  Context & Task Prioritization   │ ──▶ get_analytics(action: "summary")
               │                                 │ ──▶ manage_tasks(action: "next")
               └────────────────┬────────────────┘
                                │
                                ▼
               ┌─────────────────────────────────┐
               │  Deterministic Graph Mutation   │ ──▶ manage_nodes(action: "create"|"update")
               │  (Tasks, Decisions, Blockers)   │ ──▶ manage_edges(action: "add"|"link_visual")
               └────────────────┬────────────────┘
                                │
                                ▼
               ┌─────────────────────────────────┐
               │  Spec & Integrity Verification  │ ──▶ manage_specs(action: "compliance"|"verify")
               │                                 │ ──▶ run_diagnostics(action: "validate")
               └────────────────┬────────────────┘
                                │
                                ▼
               ┌─────────────────────────────────┐
               │  Persistent SQLite Storage      │ ──▶ .state-memory-mcp/graph.db (WAL mode)
               │  Append-Only Event Ledger       │ ──▶ SHA-256 Cryptographic Audit Chain
               └─────────────────────────────────┘

📚 Documentation Directory

Explore dedicated guides and deep dives in the docs/ directory:

GuideDescription
🏗️ Architecture & Codebase DistillationHigh-signal architectural overview, module inventory, data flows, and design decisions.
🚀 v0.10 → v1.0 Migration GuideStep-by-step migration guide, legacy tool mapping table, and STATE_MEMORY_COMPAT mode.
💡 Value Proposition & TheoryCognitive Externalization, FSM Formalism, First-Hop Determinism & Benchmark metrics.
📋 State Memory ConceptsNode Types (task, decision, blocker...), Status Values, Typed Edges & Seeding Guidelines.
⚙️ Configuration & IDE SetupAuto-Initialization details, Environment Variables table, and Editor Configs (Cursor, VS Code, Claude, Antigravity, Windsurf).
🛠️ CLI Command ReferenceCLI flags (init, run, view, inspect, metrics, audit, doctor, backup, restore, merge) & Git Scanner.
⏱️ Sessions, Snapshots & SDDSession Lifecycle, Event Audit Trail, Snapshots, Trajectories, Sub-directory support & Spec-Driven Development.
🧰 Tools, Resources & PromptsComplete reference for all 13 Consolidated MCP Tools, read-only state-memory:/// Resources, and Prompt templates.
📘 Formal API ReferenceFormal parameters, return schemas, and code signatures for all MCP endpoints.
🎨 3D Visualizer GuideViewing and exporting the interactive WebGL 3D Force-Directed Graph visualizer.
🗄️ Database SchemaSQLite tables, columns, indexes, and schema migration history.

📖 Agent Playbook: 5-Step Canonical Workflow

When an autonomous AI agent enters a repository with state-memory-mcp:

1. Orient & Bootstrap ──▶ manage_sessions(action: "start") + get_analytics(action: "summary")
2. Task Selection     ──▶ manage_tasks(action: "next") + manage_tasks(action: "find_blockers")
3. Trace Context      ──▶ query_graph(action: "trace") + manage_specs(action: "compliance")
4. Execute & Record   ──▶ manage_nodes(action: "create", type: "decision") + manage_edges(action: "link_visual")
5. Validate & Close   ──▶ run_diagnostics(action: "validate") + manage_tasks(action: "complete") + manage_sessions(action: "end")

🧪 Testing

# Run full unit, integration, and performance benchmark test suite across all 113 test files (418 tests)
npm run test

⚖️ License & Disclaimers

Developed and maintained by PuterVision. Released under the MIT License.

  • Local Storage Guarantee: All graph data, decision records, and event logs remain 100% local in your workspace. No telemetry or project data is ever transmitted.
  • Trademarks & Non-Affiliation: Product names (Cursor, Claude Code, Gemini, Windsurf, VS Code, GitHub, SQLite) are property of their respective owners and used solely for compatibility identification.

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