---
title: "Robinhood Agentic Trading Needs Receipts"
description: "How Treeship can provide local proofs, approvals, and audit trails for agents connected to Robinhood Trading MCP."
date: 2026-05-27
tags: ["mcp", "finance", "approvals", "receipts"]
readTime: "7 min read"
---

Robinhood Agentic Trading makes the boundary very real: an AI agent can connect to a dedicated brokerage account through MCP, inspect account data, reason about a strategy, and place trades.

That is exactly the kind of workflow where "the agent had access" is not enough.

The operator needs to know:

- Which agent was connected?
- Which account data did it ask for?
- What strategy or instruction was it following?
- Did it produce an order intent before execution?
- Did a human approve the action?
- What happened after the tool call?
- Can another person verify the evidence later?

That is the role Treeship should play.

## Robinhood owns execution. Treeship owns evidence.

Robinhood's architecture is MCP-native. The agent connects to Robinhood's Trading MCP endpoint from a platform like Claude Code, Codex, Cursor, ChatGPT, or another MCP-compatible runtime. Robinhood handles account authentication, Agentic account onboarding, account access, and order placement.

Treeship should not sit in that flow as a broker. It should not make recommendations. It should not custody funds. It should not pretend a receipt means a trade was suitable.

Treeship should do what trust infrastructure is supposed to do:

- record the agent identity
- record the operator intent
- record the approval boundary
- record the MCP tool call
- seal the session into a portable receipt package
- make the proof locally verifiable

In other words: Robinhood executes the trade. Treeship proves what the agent was asked to do and what evidence existed around the action.

## The dangerous gap is not just execution

Most people focus on "can the agent place an order?" That is the obvious risk, but not the only one.

Before an order, the agent may read account numbers, positions, balances, transactions, and order history. It may combine that data with external market research. It may summarize risks or produce a strategy. It may decide that an order is consistent with a user instruction.

Every one of those steps matters.

If something goes wrong, the useful question is not merely "was there a trade?" It is:

> What chain of reasoning, data access, approval, and tool execution led to that trade?

That chain should be portable. It should not live only in a model transcript, a vendor log, or a brokerage app notification.

## A receipt model for agentic trading

A good Treeship receipt for Robinhood Agentic Trading should have at least five layers.

### 1. Strategy intent

The operator or agent records the goal:

```bash
treeship attest decision \
  --actor agent://trading-operator \
  --summary "Review the dedicated Agentic account and propose a rebalance under the configured budget." \
  --confidence 0.80
```

This does not prove the strategy is good. It proves what the agent was supposed to be doing.

### 2. Data access

When the agent queries account or market data through MCP, Treeship should record the tool name, timestamp, actor, and digests of sensitive inputs and outputs.

The receipt should avoid storing raw balances or account numbers unless the operator explicitly chooses to keep them locally.

### 3. Order intent

Before execution, the agent should produce a structured order intent. This is the checkpoint where a human or policy can inspect:

- symbol
- side
- order type
- estimated notional amount
- time in force
- reason
- linked strategy prompt

### 4. Approval

For order placement, the default should be explicit approval. Treeship already has approval nonce binding: a human approval produces a nonce, and the execution receipt must echo it.

```bash
treeship attest approval \
  --approver human://operator \
  --description "Approve one Robinhood Agentic account order under the reviewed budget."
```

The nonce prevents a generic "approval" from becoming a reusable blank check.

### 5. Execution receipt

After the MCP tool call, Treeship records the tool result digest, status, elapsed time, actor, and session context. The sealed `.treeship` package then becomes a local audit artifact.

## The dashboard should start with Treeships, not files

This is also why the local dashboard should be organized around Treeship instances.

A Treeship is the trust container. Inside it are agents, sessions, receipts, reports, approvals, and proof packages. For a Robinhood setup, a user should be able to open the dashboard and see:

- the trading-agent Treeship
- which agents are attached
- which receipts each agent produced
- whether any order-placement action is missing approval
- which reports are ready to review
- whether Hub sharing is disabled or enabled

That mental model is much clearer than a flat folder of receipt files.

## Recommended default posture

For agentic trading, Treeship should be conservative by default:

- portfolio/account reads: receipt and hash sensitive payloads
- market-data reads: receipt with source and timestamp
- order intent: require a decision artifact
- order placement: require human approval nonce
- automation: require a session policy and review window
- sharing: redact or hash sensitive account details

This is not about slowing agents down for its own sake. It is about making the agent's authority explicit and reviewable.

## What Treeship should build next

The first version can be mostly documentation and templates:

- a Robinhood Agentic Trading integration guide
- a policy template that marks order placement as approval-required
- dashboard sections that show Treeship -> agents -> receipts -> reports
- receipt rendering that highlights strategy intent, approval, and execution evidence

The second version should understand Robinhood MCP activity more specifically:

- classify tools as account read, market read, order preview, or order placement
- flag order placement without an approval nonce
- show trading sessions in the local dashboard review queue
- produce sanitized reports suitable for sharing

The goal is not to make Treeship a trading platform.

The goal is to make agentic trading auditable enough that builders, developers, and operators can answer the uncomfortable questions after the fact:

- what did the agent know?
- what did it decide?
- who approved it?
- what did it do?
- can we prove it?

That is what receipts are for.
