T1/T2 Protocol MCP server
io.github.Fauxetine/t1-t2-protocol·v0.1.1Caller-side T1/T2 heterogeneous validation MCP server. Stdlib-only Python.
0 stars22 downloads/wk
Reviews
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T1/T2 Protocol tools
No tool declarations could be read from the package source. They show once the server is installed.
Public scan report
scanner v0.1.9 · 2026-09-26 · same rubric, same numbers if you re-run it
- Code scan2 source files scanned25/25
- –Live reliabilityno gateway calls yet and no remote to proben/a
- –Tool poisoningtools not inspected (local package is not executed); not countedn/a
- Auth qualitylocal package, no credentials required12/15
- Maintenancelast push 103 days ago8/15
- Maintainer identityregistry namespace matches repository owner6/10
How T1/T2 Protocol compares
Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.
- Safety score
- 78/100
- higher than 57% of 48,255 servers
- Downloads a week
- 22
- more than 8% of 12,191 servers with a package
Install T1/T2 Protocol in Claude Code, Cursor or VS Code
claude mcp add t1-t2-protocol -- uvx t1-t2-protocol
What the publisher says
From the T1/T2 Protocol repository's README, as published. We do not edit it. Read it on GitHub
T1/T2 Protocol — Heterogeneous Validation for MCP
中文文档 · MCP Registry entry (io.github.Fauxetine/t1-t2-protocol)
<!-- mcp-name: io.github.Fauxetine/t1-t2-protocol -->
Reference implementation. This is a stdlib-only MCP reference server for structured reasoning discipline — not a production security product. Evaluate your own threat model before deploying in sensitive environments. Unlike official Python MCP servers, it does not use the mcp SDK; it speaks JSON-RPC over stdio directly.
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
How tools return data: t1protocol and t2protocol return structured prompt templates for your MCP host's LLM to execute. Only checksum returns deterministic JSON (checksum_passed, errors).
Tool inputs (MCP schema)
t1_protocol
t2_protocol
checksum
Returns JSON: {"checksum_passed": bool, "errors": [...]}.
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>=0.1.0"From source (development):
Shortened. The full README is on GitHub.
Nothing above is checked by us. What we check is on the safety report.
T1/T2 Protocol: common questions
- Is T1/T2 Protocol MCP server safe?
- Mostly: it is graded B (78/100). Read the T1/T2 Protocol safety report
- How do I install T1/T2 Protocol?
- It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
- Does T1/T2 Protocol need an API key?
- Not as far as the registry entry and our scan can tell: no credentials are declared or required.
- Is T1/T2 Protocol maintained?
- The last commit was 141 days ago (2026-06-16). The latest release is v0.1.1.
- How does T1/T2 Protocol compare with other MCP servers?
- safety score 78/100 (higher than 57% of 48,255 servers); downloads a week 22 (more than 8% of 12,191 servers with a package). Full comparison.