Ownership & Provenance Protocol

Ownership & Provenance Protocol

An MCP server that enforces explicit task ownership acceptance and requires a provenance tag (observed, reviewed, or reported) on every completion claim, preventing silent inheritance and unverified assertions in multi-agent systems.

Category
Visit Server

README

Ownership & Provenance Protocol (OPP)

A small MCP protocol that enforces two rules no mainstream agent framework currently enforces natively:

  1. A task isn't owned until it's explicitly accepted. Assignment, inference, or ambient context aren't enough — an actor (human or AI agent) must explicitly accept a task before it counts as theirs.
  2. No completion claim can be reported without saying how the reporting actor knows it's true. Every "this is done" claim carries a mandatory provenance tag: Observed (I did this myself), Reviewed (I checked evidence of it), or Reported (I'm relaying what I was told). There is no fourth option, and there is no way to report completion without picking one.

That's the whole protocol. Everything else in this repo exists in service of those two rules.

Why this exists

Broken ownership handoffs are named as the #1 failure mode in production multi-agent systems — an agent hands work to another agent, context gets lost, and nobody ends up owning the result. Separately, a 2026 academic paper on agent delegation authority found that no implemented protocol combines authority-scoped delegation with provenance-aware completion records — agents can claim things are true without any structural requirement to say how they know.

This project was built to test a narrow, specific fix for both problems at once — not a full governance platform, not an evaluation framework, just the ownership/acceptance and provenance mechanics.

Real-world motivation

Two documented, public incidents shaped this design directly:

  • A Replit AI coding agent deleted a production database during an active code freeze, then falsely claimed rollback was impossible — a claim stated with full confidence that was never actually verified. Rollback worked fine once someone actually tried it.
  • Air Canada's chatbot invented a bereavement-fare policy that didn't exist and told a grieving customer he qualified for it. A tribunal ruled the airline liable — establishing that companies own what their AI tells people, whether or not a human ever reviewed the claim first.

Both failures are the same shape: a claim stated with more confidence than it had earned. That's the specific thing this protocol is built to catch — not by making agents smarter, but by making it structurally impossible to report a claim without saying how it was actually verified.

The six MCP tools

Tool What it does
create_task Creates a task in pending_acceptance state. Nothing is owned yet.
accept_task Explicit acceptance. Fails if the task already has a different current owner — enforced atomically, safe under real concurrency, not just sequential calls.
report_completion Requires a valid provenance value (observed | reviewed | reported). Rejects the call outright if it's missing or invalid — this is the one non-negotiable rule in the whole system.
handoff_task Transfers ownership, capturing a frozen snapshot of task state at the exact moment of transfer. The recipient does not automatically become the owner — they must call accept_task themselves before they can act.
get_task_status Returns the complete history for one task: injection, all acceptances, all completion claims with their provenance, all handoffs.
list_unaccepted_tasks Surfaces tasks with no current owner — a direct, queryable signal for exactly the "silent inheritance" risk this protocol exists to prevent.

Protocol invariants

These six statements hold in any conformant implementation, at all times:

  1. Every task has zero or one current owner.
  2. Ownership changes only through explicit Acceptance.
  3. Every completion claim has exactly one provenance value.
  4. Provenance values never become "more certain" without new evidence — an actor cannot upgrade a Reported claim to Observed without an intervening act of verification.
  5. Every handoff captures a state snapshot.
  6. Every task's history is fully reconstructable.

An implementation is OPP-conformant if it requires explicit acceptance, rejects claims without provenance, supports all three provenance categories, preserves reconstructable history, and supports ownership transfer via handoff. Partial implementations should describe themselves as "OPP-inspired," not conformant.

Why not just use logs / Git / OpenTelemetry / Temporal?

Fair question, and the short answer is that OPP is meant to sit alongside these, not replace them:

  • Logs record what happened passively, after the fact. OPP requires an active commitment before a claim is accepted as complete.
  • Git versions content and records who committed what, but has no concept of an accepted, ongoing obligation separate from the artifact itself.
  • OpenTelemetry propagates context through a distributed trace excellently, but has no schema concept equivalent to a provenance tag on a claim of correctness.
  • Temporal / durable execution engines solve state persistence and reliable resumption extremely well — this reference implementation is meant to be built on top of something like that, not reinvent it. But durable execution answers "did this step run," not "did the actor own this task by explicit acceptance, and how do we know its completion claim is true."

Running it locally

Requirements: Node.js 24, PostgreSQL, pnpm.

pnpm install

Set the required environment variables:

DATABASE_URL=<your Postgres connection string>
MCP_ACCESS_TOKEN=<a long random string — generate one with `openssl rand -hex 32`>

Push the schema and start the API server:

pnpm --filter @workspace/db run push
pnpm --filter @workspace/api-server run dev

The MCP server is mounted at /mcp and requires the MCP_ACCESS_TOKEN as a bearer token in the Authorization header on every request except /api/healthz, which stays open for health checks.

Connect a real MCP client (e.g. Claude Code):

claude mcp add --transport http opp http://localhost:5000/mcp --header "Authorization: Bearer <your-token>"

Status

This is an early, personally-tested project, not a polished product. It has been built and adversarially tested by hand — concurrency races, malformed inputs, ownership-bypass attempts — and verified end-to-end with a real external MCP client completing the full protocol handshake against a live deployment. It has not been used by anyone beyond its author, and no claims are made about production-readiness beyond what's described above. If you use this and find something that breaks, or a case the invariants don't cover, please open an issue.

License

MIT

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured