t1-t2-protocol 2.5.3

Heterogeneous validation protocol for MCP — structured reasoning (T1) + cross-model confidence evaluation (T2) + deterministic checksum

T1/T2 Protocol — Heterogeneous Validation for MCP

T1/T2 is an MCP server that makes AI reasoning verifiable, auditable, and trustworthy — by decomposing ambiguous questions into structured tiers (T1), then validating answers through cross-model evaluation (T2), with a deterministic checksum layer that doesn't depend on any LLM.

Why?

When an LLM checks its own answer, it uses the same training data, the same reasoning preferences, and the same systematic biases. Self-reflection cannot catch its own blind spots.

T1/T2 introduces heterogeneous validation: the model that produces the answer and the model that evaluates it should be different. Their different training distributions cover each other's blind spots.

Tools

Tool	Function	Why it matters
t1_protocol	Decomposes ambiguous questions into L1 (facts) / L2 (assumptions) / L3 (hypotheses) / L4 (unknowns)	Forces structured reasoning before answering
t2_protocol	Evaluates answer quality from another model's perspective	Catches blind spots self-reflection misses
checksum	Deterministic structural validation — pure regex, zero LLM dependency	Safety that doesn't scale with intelligence

Quick Start

Requirements

• Python 3.10+
• An MCP client: Cursor, Claude Desktop, Windsurf, or any MCP-compatible host

Install

From PyPI (recommended):

pip install t1-t2-protocol

From source (development):

git clone https://github.com/Fauxetine/t1-t2-protocol.git
cd t1-t2-protocol
pip install -e ".[dev]"
python -m pytest tests/ -v

Or run directly without installing:

python3 src/t1_t2_mcp_server.py

After pip install, register the console script in MCP config:

{
  "mcpServers": {
    "t1-t2-protocol": {
      "type": "stdio",
      "command": "t1-t2-protocol"
    }
  }
}

Configure

Cursor — add to .cursor/mcp.json:

{
  "mcpServers": {
    "t1-t2-protocol": {
      "type": "stdio",
      "command": "python3",
      "args": ["/path/to/src/t1_t2_mcp_server.py"]
    }
  }
}

Claude Desktop — add to claude_desktop_config.json:

{
  "mcpServers": {
    "t1-t2-protocol": {
      "command": "python3",
      "args": ["/path/to/src/t1_t2_mcp_server.py"]
    }
  }
}

Usage

T1: Structure a vague question

Call t1_protocol with your question. It returns a structured prompt with four tiers:

Input: "Should we migrate our monolith to microservices?"

Output: Structured prompt with:
  [L1 Facts]     Team size, codebase size, current stack
  [L2 Assumptions] Expected benefits that need verification
  [L3 Hypotheses] Testable claims about migration risk
  [L4 Unknown]   Future growth trajectory
  [Core Question] The precise feasibility question

T2: Cross-validate a decision

Call t2_protocol with a decision or answer text. It returns confidence assessment + adoption recommendations:

Input: "Decision text for approach A..."

Output:
  Confidence: Medium-High
  Adoption table with:
    ✅ Adopt     — verified conclusions (L1)
    ⚠️ Reserved — needs more evidence (L2)
    ❌ N/A      — blind spots to address

checksum: Validate output structure

Call checksum with structured text. It returns pass/fail based on deterministic rules:

Input: "[L1 Facts]\n1. ...\n[L2 Assumptions]\n1. ...\n---"
Output: {"checksum_passed": true, "errors": []}

Full pipeline

Vague question → T1 structured decomposition → Decision based on structure → checksum (optional) → T2 validation → Refined decision

For time-sensitive factual claims, search on the caller side before T2 — see Caller-side web verification (v2.6).

Configuration

Locale

Both t1_protocol and t2_protocol accept an optional locale parameter:

Value	Output
en (default)	English templates
zh	Chinese templates

Example: {"question": "...", "locale": "zh"}

Weight hints

Both t1_protocol and t2_protocol accept an optional weight_hint parameter to bias evaluation criteria:

Weight	Effect
事实优先 / fact-first	Prioritizes factual accuracy
效率优先 / efficiency-first	Prioritizes efficiency
成本优先 / cost-first	Prioritizes cost
鲁棒性优先 / robustness-first	Prioritizes robustness
通用优先 / general-first	No specific bias

Recursion protection

T2 automatically detects recursion depth and terminates at depth >= 3, where marginal information gain drops below 5%.

Design Philosophy

See docs/philosophy.md for the full design rationale.

Core tenets:

1. Separate intelligence from trust — AI capability and AI safety should be guaranteed by different systems
2. Heterogeneous over self-referential — Cross-model validation is more reliable than self-reflection
3. Deterministic over probabilistic — What can be checked by code should not be left to model judgment

Examples

See examples/ for step-by-step walkthroughs:

• T1: Structure a vague question
• T2: Cross-validate a decision
• Full pipeline: T1 → decision → T2

Positioning

Project	Layer	What it does	T1/T2 relationship
Sequential Thinking (official MCP)	Caller-side chain-of-thought	One model logs iterative steps	Complementary — T1 adds L1–L4 tiers + T2 cross-model review
ThoughtProof / verdict APIs	Server-side verification	APPROVE/DENY/UNCERTAIN with confidence	Complementary — T1/T2 structures reasoning before verdict APIs act
Self-reflection / prompt chains	Same model	Re-reads or re-prompts its own output	Replaced — heterogeneous validation catches shared blind spots
Tool integrity (e.g. Phionyx)	Transport / tool schema	Detects tool poisoning, schema drift	Orthogonal — T1/T2 does not secure tool definitions

T1/T2 is a stdlib reference implementation for MCP Discussion #2574-style reasoning discipline: structure first (T1), cross-validate second (T2), checksum what code can verify. It is not a signed verdict API and not a security scanner.

License

MIT — see LICENSE.

---

Built for the MCP ecosystem. Part of a broader exploration into AI safety through deterministic architecture.

---

Links

• Contributing
• MCP Registry
• Security policy
• Changelog
• Design philosophy
• Caller-side web verification v2.6
• Agent / MCP host instructions