@helm-protocol/ttt-mcp

Reference implementation of draft-helmprotocol-tttps (IETF Experimental)

MCP Server for OpenTTT — Proof of Time tools for AI agents

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The Problem: Workflow Amnesia

Every Claude Code long-horizon workflow hits the same wall: context compression erases action history.

Agent B has no memory of what Agent A decided. Agent A resumes after compression with no record of its own prior steps. Duplicate work. Lost decisions. State corruption.

ttt-mcp is the external causal chain that survives context compression.

Every workflow step is anchored to a cryptographic timestamp on an external server — physically separate from Claude's context window. When compression happens, agents call pot_query(eventId) for O(1) exact step recall and resume with full causal context.

Claude workflow → [context compressed] → agents call pot_query(eventId)
                                         → external server returns full timeline
                                         → workflow resumes, zero lost state

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Mathematical Guarantees

Layer	Mechanism	Guarantee
Identity	SHA-3 eventId (256-bit)	Collision probability 2⁻²⁵⁶ — practically zero
Lookup	O(1) exact retrieval	No context consumed by history reconstruction
Ordering	TTTPS causal timestamps	Total order on events — tamper-proof sequence proof
Causal chain	prevEventId DAG	O(depth) traversal — depth ~100 for 1B-token workflows
Non-repudiation	Ed25519 signature	Cryptographic proof of who acted when
Resilience	Erasure-coded cryptographic shards	≥97% recovery at BER=0.05, 99.88% at BER=0.02 (theoretical)
Persistence	Redis AOF + 90-day TTL	Server survives context compression and restarts

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Quick Start

Claude Code

claude mcp add ttt -- npx -y @helm-protocol/ttt-mcp@0.3.0

With an API key (raises the free limit to your plan's monthly quota):

claude mcp add ttt -e TTT_API_KEY=your-key -- npx -y @helm-protocol/ttt-mcp@0.3.0

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "ttt": {
      "command": "npx",
      "args": ["-y", "@helm-protocol/ttt-mcp@0.3.0"],
      "env": { "TTT_API_KEY": "your-key" }
    }
  }
}

Cursor

One-click install, or add the same mcpServers block above to .cursor/mcp.json.

Free tier: 100 calls/day per IP — no signup needed.

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5-Minute Test

Once connected, run this sequence in Claude:

Step 1 — Stamp a workflow step:

Just tell Claude naturally:

"Stamp this step as my-first-step" "Record what I just did as refactor-auth-step1"

Claude calls pot_generate automatically. Or call it directly:

pot_generate(eventId: "my-first-step")

Step 2 — Simulate context compression: start a new Claude session

Step 3 — Recover in the new session:

Tell Claude:

"What did I do in my-first-step?" "Recover my last workflow state"

Or call directly:

pot_query(eventId: "my-first-step")

→ Returns exact record. Amnesia gone.

Step 4 — Build a causal chain:

pot_generate(eventId: "step-2", prevEventId: "my-first-step")
pot_graph(eventId: "step-2", depth: 5)

→ Full backward chain. Cryptographically ordered.

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7 Tools

Tool	Purpose
pot_generate	Stamp a workflow step with a cryptographic timestamp
pot_verify	Verify a PoT signature
pot_query	O(1) exact lookup by eventId — core amnesia recovery
pot_graph	Traverse causal DAG (backward + forward chain)
pot_checkpoint	Roll up events into a compressed summary — use every ~100 events or before long tasks
pot_stats	Server statistics and mode status
pot_health	Health check

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Tool Parameters

pot_generate

Stamp a workflow step with a cryptographic timestamp. For Claude Code: use eventId + prevEventId. For DeFi: use txHash + chainId + poolAddress. One of eventId or txHash is required.

Parameter	Type	Required	Description
eventId	string	Either/or	Workflow step identifier. E.g. "refactor_auth_step1"
prevEventId	string	No	Previous step's eventId — links steps into a causal chain
txHash	string	Either/or	Transaction hash (DeFi, hex with 0x prefix)
chainId	number	No	EVM chain ID (DeFi)
poolAddress	string	No	DEX pool contract address (DeFi)

pot_query

Query Proof of Time records. Use eventId for O(1) exact lookup after context compression.

Parameter	Type	Required	Description
eventId	string	No	Exact step lookup — collision probability 2⁻²⁵⁶
startTime	number	No	Start time (unix ms). Default: 24h ago
endTime	number	No	End time (unix ms). Default: now
limit	number	No	Max entries to return. Default: 100, max: 1000

pot_graph

Traverse the causal chain from any step. Returns backward chain (ancestors) and forward chain (descendants).

Parameter	Type	Required	Description
eventId	string	Yes	Step to traverse from
depth	number	No	Max backward depth. Default: 10, max: 100

Returns:

• backwardChain — ancestors in chronological order (depth-compressed for large chains)
• forwardChain — steps that follow the given eventId
• chainBroken — true if a gap is detected (ancestor was evicted from ring buffer, or the chain root references an unknown entry)
• brokenAt — "server_restart" if the gap was caused by a server restart clearing in-memory state; otherwise the eventId at which the break occurred; null if chain is intact
• reachableDepth — number of ancestors successfully traversed before the gap (or chain root)

Causal chain gap causes:

• server_restart: the server restarted and the in-memory DAG was cleared. If Redis is available and REDIS_URL is set, the DAG is rebuilt from Redis on startup — reducing restart gaps.
• Ring-buffer eviction: the ring buffer holds the most recent 10,000 events in memory. Ancestors beyond that window show as chainBroken: true with brokenAt set to the oldest reachable eventId.

Recovering from a gap: call pot_checkpoint before long tasks to compress and preserve the chain within the token budget, or use Redis persistence to survive restarts.

pot_verify

Parameter	Type	Required	Description
potHash	string	Yes	PoT hash to verify (hex with 0x prefix)
grgShards	string[]	Yes	Array of hex-encoded cryptographic integrity shards
chainId	number	Yes	EVM chain ID
poolAddress	string	Yes	Uniswap V4 pool address

pot_stats

Parameter	Type	Required	Description
period	"day" | "week" | "month"	Yes	Time period for statistics

pot_health

No parameters.

pot_checkpoint

Creates a compressed rollup checkpoint of workflow history.

Use when: Approaching context limit, before long tasks, or every ~100 events.

Parameter	Type	Required	Description
fromEventId	string	No	Start of range — first eventId in the causal chain to include
toEventId	string	No	End of range — last eventId in the causal chain to include
startTime	number	No	Unix ms. Default: 1 hour ago
endTime	number	No	Unix ms. Default: now
maxTokens	number	No	Approximate max tokens for rollup output. Default: 2000

Returns:

• checkpointId — unique checkpoint identifier
• rollup — compressed event history (depth-adaptive: full/compact/minimal/rollup)
• summary — human-readable one-line summary of the checkpoint
• chainIntact — whether the causal chain is unbroken
• nextCheckpointHint — recommended events before next checkpoint

Depth-adaptive compression:

Depth	Format	~Tokens
1–5	Full entry	~200/event
6–20	Compact (id+hash+ts)	~80/event
21–50	Minimal (id+ts)	~30/event
51+	Rollup string	~10/event

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Use Cases

1. Claude Code Workflow — Amnesia Prevention

Problem: A 20-agent Dynamic Workflow refactors a 500K-line codebase over hours. After each context compression, agents have no memory of what they already processed. Duplicate work. State corruption.

Solution: Each agent stamps its steps with pot_generate(eventId, prevEventId). After compression, it calls pot_query(eventId) to recover its exact action history — what ran, when, in what order — from the external server. The server is outside Claude's context window; compression never touches it.

// Agent starts a workflow step
const pot = await client.callTool({
  name: "pot_generate",
  arguments: {
    eventId: "refactor_auth_module_step3",
    prevEventId: "refactor_auth_module_step2"
  }
});
// pot.potHash — cryptographic proof this step happened at this time

// After context compression, agent recovers its history:
const history = await client.callTool({
  name: "pot_query",
  arguments: { eventId: "refactor_auth_module_step3" }
});
// history.local[0] — exact record: timestamp, prevEventId, potHash
// history.found: true — O(1) lookup, collision probability 2⁻²⁵⁶

// Traverse full causal chain:
const chain = await client.callTool({
  name: "pot_graph",
  arguments: { eventId: "refactor_auth_module_step3", depth: 20 }
});
// chain.backwardChain — all ancestor steps in chronological order
// chain.forwardChain — steps that follow this one
// chain.chainBroken — true if a gap was detected in the ancestor chain
// chain.brokenAt    — "server_restart" if the server restarted and cleared
//                     the in-memory DAG; otherwise the eventId of the oldest
//                     reachable ancestor before the gap; null if chain intact
// chain.reachableDepth — how many ancestors were recovered before the gap

// Handle a server-restart gap:
if (chain.chainBroken && chain.brokenAt === "server_restart") {
  // Server cleared in-memory state; ancestors before the gap are gone unless
  // Redis was configured (REDIS_URL) — in that case the DAG was rebuilt on
  // restart and chainBroken will be false.
  // Recover by querying the most recent checkpoint or restarting from a known step.
}

Before a long task or every ~100 events — create a checkpoint:

// Compress workflow history before context fills up — by causal range:
const checkpoint = await client.callTool({
  name: "pot_checkpoint",
  arguments: {
    fromEventId: "refactor_auth_module_step1",
    toEventId: "refactor_auth_module_step3"
  }
});
// checkpoint.checkpointId — store this; resume from it after compression
// checkpoint.rollup — depth-adaptive compressed history (10–200 tokens/event)
// checkpoint.chainIntact: true — causal chain verified unbroken
// checkpoint.nextCheckpointHint: 87 — suggested events before next checkpoint

// Or compress by time window with a token budget:
const checkpoint = await client.callTool({
  name: "pot_checkpoint",
  arguments: {
    startTime: Date.now() - 3_600_000,  // last 1 hour
    maxTokens: 1500
  }
});

// After context compression, restore from checkpoint instead of re-querying all events:
const history = await client.callTool({
  name: "pot_query",
  arguments: { eventId: checkpoint.checkpointId }
});
// Full causal context restored in a single call

Outcome: Zero duplicate work. Full workflow timeline recoverable even after complete context resets.

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2. MEV Bot — Transaction Ordering Proof

Problem: You got front-run. You can't prove it — mempool timestamps are per-node, unsigned, non-authoritative.

Solution: Call pot_generate before every submission. The PoT receipt is cryptographically signed using three independent time sources (NIST, Google, Cloudflare). The on-chain hash can be anchored via a separate Base Sepolia TTT ERC-1155 contract. If front-running occurs, you have a timestamped record predating the attacker's block inclusion.

const pot = await client.callTool({
  name: "pot_generate",
  arguments: { txHash: pendingTxHash, chainId: 8453, poolAddress: "0x..." }
});
// pot.potHash — your evidence, timestamped by NIST+Google+Cloudflare

Note: The DeFi path (txHash + chainId + poolAddress) requires a server-side build with the integrity-shard pipeline enabled. It is not available in the public openttt npm package; calls without it will throw. The Claude Code path (eventId) works out of the box.

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3. DEX Protocol — Sandwich Deterrence

Solution: Integrate TTTHookSimple (Uniswap V4 hook, Base Sepolia: 0x8C633b05b833a476925F7d9818da6E215760F2c7). Honest builders get turbo mode. Tampered sequences get full mode (penalty delay). Economics, not governance.

Note: Shard-based verification (pot_verify with grgShards) requires a server-side build with the integrity-shard pipeline enabled — not available in the public openttt npm package.

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4. Hedge Fund / Prop Desk — MiFIR Art.22c Compliance

Problem: MiFIR Article 22c / RTS 25 requires microsecond-precision UTC-synchronized timestamps. Hardware PTP appliances cost $50K–$500K.

Solution: pot_generate produces an Ed25519-signed timestamp with an uncertainty bound and multi-source attestation. Structurally compatible with the RTS 25 audit record format. One API call per trade.

const audit = await client.callTool({
  name: "pot_generate",
  arguments: { txHash: tradeHash, chainId: 8453 }
});
// audit.timestamp: high-resolution timestamp
// audit.uncertainty: ± bound (RTS 25 uncertainty field)
// audit.confidence: fraction of sources that agreed

Precision note: The default network time sources (Roughtime / NTP) provide a few-millisecond uncertainty bound. The MiFIR Art. 22c / RTS 25 ±1ms (and tighter) requirement is met only with an added GEO time source (KTSat); this is a roadmap configuration, not the default deployment.

Outcome: Structurally compatible audit trail. IETF specification: draft-helmprotocol-tttps.

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5. Multi-Agent Coordination — Causal Order Proof

Problem: When multiple AI agents interact in a pipeline, the causal order matters for debugging and audit. Agent logs are unverifiable.

Solution: Each agent stamps its action with pot_generate. The potHash chain is independently verifiable. pot_graph reconstructs who did what and in what order.

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How It Differs — A Different Job, Not "Better"

Tool	Integration	What it recalls	Integrity	Hot-path cost
Letta (MemGPT)	owns the agent loop	self-editing semantic memory	none	embedding + vector search per memory op
LangGraph / LangMem	LangGraph only	graph state / semantic	none	checkpoint I/O (+ embeddings)
RAG / vector DB	bolt-on	fuzzy similarity	none	embed + vector search per item
ttt-mcp	2-min MCP retrofit	exact causal step (by eventId)	Ed25519 + TTTPS timestamp	sign + hash + write — 0 embedding calls

The cost difference is structural, not incidental.

Letta and Mem0 treat agent memory as a semantic search problem — every recall forces an LLM embedding call and a vector search. ttt-mcp bypasses the LLM/embedding layer entirely: state recovery is an O(1) cryptographic hash lookup. Marginal cost is commodity CPU + storage, not API tokens.

Scope: agents stamp the steps worth checkpointing — not every token, not every query. Volume tracks decisions, not total chat traffic.

If you need fuzzy semantic search over past conversations, use Letta or a vector DB. If you need a zero-embedding, deterministic state recovery layer for long-horizon workflows that survives context compaction, use ttt-mcp.

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Pricing

Tier	Price	Calls/month
Free	$0	100/day per IP — no signup
Dev	$29/mo	100K
Pro	$99/mo	1M
Team	$299/mo	10M + $0.01/1K overage
Enterprise	$999+/mo	100M calls/mo · $0.001/1K overage · SLA 99.9%
Platform License	Negotiated ($2M+/yr)	Volume cap negotiated · native integration

Subscribe:

Dev $29/mo · Pro $99/mo · Team $299/mo — to subscribe, email peter@kenosian.com.

Enterprise & Platform License: peter@kenosian.com

Contact: peter@kenosian.com

Quota mechanics — stdio vs HTTP:

• HTTP mode (Glama / Smithery container, PORT set): the per-IP free tier limit (100 calls/day) is enforced locally in the server process.
• stdio mode (Claude Code npx, Claude Desktop): there is no per-IP counter. Tool calls are delegated to api.kenosian.com via X-TTT-API-Key; quota is enforced server-side against your plan's monthly allowance. Without TTT_API_KEY the local fallback runs with no daily cap, but plan features (server-side DAG persistence, multi-session causal chains) are unavailable.

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Requirements

• Node.js >= 18
• Network access for time synthesis (HTTPS to time.nist.gov, time.google.com, time.cloudflare.com)

Time source tiers (automatic fallback):

Tier	Source	Stratum	Notes
1 (preferred)	PTP / hardware clock	0–1	Requires local PTP daemon
2	Roughtime / NTP (NIST, Google, Cloudflare)	2–4	Default for most deployments
3 (offline fallback)	Local system clock	16	RFC 5905 unsynchronized stratum — used when all network sources are unreachable

The server falls through to stratum 16 automatically; no manual configuration needed. The stratum field in every pot_generate response indicates which tier was used.

Redis persistence (optional):

Redis is not required. The in-memory DAG is authoritative at runtime. If REDIS_URL is set, events are written to Redis with a 90-day TTL and the DAG is rebuilt from Redis on server restart — reducing server_restart chain gaps. Without Redis, the in-memory DAG is cleared on restart.

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Production Tips

Cold Start warm-up — On first startup, BatchSigner requires one request to initialize. Call pot_health or send a single dummy pot_generate before your load balancer health check goes live. Without this, the first request may see p99 ~500ms; subsequent requests stabilize to <10ms.

# Kubernetes / Docker: add to your startup script
curl -s http://your-server/pot/health > /dev/null

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Learn More

• OpenTTT SDK — The underlying SDK
• IETF Draft: draft-helmprotocol-tttps — TTTPS Protocol Specification
• Helm Protocol — GitHub

License

BSL-1.1 — free for non-commercial use.

Commercial use (production bots, hedge funds, prop desks) requires a license.

Change Date: 2029-05-28 → Apache 2.0