---
type: "post"
id: "getting-the-source-right-not-just-the-fact-829a2e"
slug: "getting-the-source-right-not-just-the-fact-829a2e"
title: "Getting the Source Right, Not Just the Fact: Source-Aware Verification for MCP Agents"
description: "Multiverse Computing published ProvenanceGuard, a post-generation verification layer for MCP-based LLM agents."
retrieval_nugget: "Multiverse Computing published ProvenanceGuard, a post-generation verification layer for MCP-based LLM agents."
published_at: "2026-10-23"
updated_at: "2026-10-23"
record_date: "2026-10-23"
date_kind: "published_at"
topics: ["mcp"]
entities: ["Multiverse Computing","ProvenanceGuard","Model Context Protocol","MiniLM","DeBERTa","RAGAS"]
related_patterns: ["verification-bandwidth-is-the-scarce-resource"]
novelty_label: "new_fact"
what_changed: "Introduces ProvenanceGuard, a source-aware verifier for MCP agents that detects claims attributed to the wrong tool output."
prior_refs: ["memory:d2484d3d-e4af-8210-a188-9f63c87e9e02"]
source_url: "https://huggingface.co/blog/MultiverseComputingCAI/getting-the-source-right-not-just-the-fact-source"
source_urls: ["https://huggingface.co/blog/MultiverseComputingCAI/getting-the-source-right-not-just-the-fact-source"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:getting-the-source-right-not-just-the-fact-829a2e"
status: "published"
visuals: []
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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---

# Getting the Source Right, Not Just the Fact: Source-Aware Verification for MCP Agents

## Retrieval answer

Multiverse Computing published ProvenanceGuard, a post-generation verification layer for MCP-based LLM agents.

Multiverse Computing published ProvenanceGuard, a post-generation verification layer for MCP-based LLM agents. The method keeps tool-output source IDs separate rather than pooling evidence, then decomposes an answer into claims, routes each claim to a source, checks support and attribution, and returns per-claim verdicts plus an allow-or-block decision.

The reported evaluation used 281 traces from a medical agent. Human experts reviewed 361 claims from 40 held-out answers: 139 claims were judged unsuitable to pass, and ProvenanceGuard caught 138; it also held 67 claims the experts judged supported. For claims with an identifiable source, the article reports about 86% exact-source selection. On a 50-case controlled attribution-swap test, it detected all swaps.

The local experimental configuration used MiniLM for source routing, a DeBERTa NLI model for support checks, and a local language model for claim decomposition. The article reports roughly half a second of overhead per answer. It does not establish performance on production deployments, hosted-model configurations, or domains beyond the reported medical-agent tests; it also reports 50.3% exact-source identification in a harder similar-source test.

Source: https://huggingface.co/blog/MultiverseComputingCAI/getting-the-source-right-not-just-the-fact-source

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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