[UC] Cross-Platform Trust Scoring for Agent Authorization Decisions

## Use Case

A platform operator runs an agent marketplace where third-party agents request access to tools, data, and payment capabilities. Before granting access, the operator needs to answer: "Should this agent be trusted for this action, given its history across all platforms it has operated on?"

Today this is answered through ad-hoc mechanisms — API key allowlists, manual review, or platform-specific reputation scores that don't port. An agent's demonstrated reliability on Platform A carries zero verifiable weight on Platform B.

## Problem

- 20% of AI skills on ClawHub (the largest public agent marketplace) are malicious
- 41% of MCP servers lack authentication
- No standard exists for portable, queryable trust scores across agent platforms
- MCP-I solves "who is this agent?" — but not "should you trust it?"

## Proposed Approach

A trust scoring protocol that complements MCP-I by defining:

- A standard schema for multi-dimensional trust scores (verification, tenure, performance, economic backing, community endorsement, behavioral consistency)
- A JSON-RPC 2.0 query protocol for trust verification (`trust/query`, `trust/verify`, `trust/history`)
- Composability with MCP-I: DID-based subject identifiers when MCP-I is available
- Multiple conformance levels from read-only queries to zero-knowledge trust proofs
- Transport-agnostic bindings (HTTPS, Nostr, IPFS, SSE)

We've drafted an open specification for this: [MCP-T](https://github.com/Percival-Labs/mcp-t) (CC-BY-4.0).

## Relationship to TAAWG

MCP-I answers identity. MCP-T answers trust. Together they form the verification stack for agent authorization decisions. We'd welcome feedback from the working group on the spec and are interested in exploring whether this fits as a TAAWG work item.

## Design Requirements Addressed

- Cross-platform agent reputation portability
- Machine-readable trust signals for automated authorization
- Privacy-preserving verification (ZK proofs at higher conformance levels)
- Implementation-agnostic (defines format, not scoring algorithm)