# Tokenized Stock

> Use this tool when you need to automate stock trading with a secure and controlled budget, enabling AI agents to buy and sell tokenized US equities with daily USD caps and sandbox testing. It solves problems of excessive trading losses and provides a safe environment for testing trading strategies. The tool accepts stock symbols and budget configurations as inputs and returns trade execution status and account balances as outputs.

Canonical page: https://skillsregistry.net/skills/evidai-tokenized-stock  
JSON: https://api.skillsregistry.net/v1/skills/evidai-tokenized-stock

## Description

Tokenized Stock enables AI agents to purchase and sell tokenized US equities through Dinari's dShares platform, settled in USDC. A server-enforced daily USD cap blocks trades that would exceed the configured budget, and sandbox mode is active by default. Seven tools cover supported stock listing, quote lookup, guarded buy and sell operations, and cap management.

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-09-01

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-01

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/evidai-tokenized-stock)
- **Repository:** <https://github.com/evidai/agent-payment-mcp/tree/HEAD/tokenized-stock-mcp>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "evidai-tokenized-stock"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/evidai-tokenized-stock` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/evidai-tokenized-stock/pull`

---
SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt