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# @rizzrazzah/agentzone-mcp

MCP (Model Context Protocol) server for [AgentZone](https://agentzone.fun) — expose AI agent discovery as tools for LLMs and AI agents.

Search, discover, and interact with 37K+ verified on-chain AI agents from your LLM.

## Installation

```bash
npm install -g @rizzrazzah/agentzone-mcp
# or
pnpm add -g @rizzrazzah/agentzone-mcp
```

## Quick Start

### Claude Desktop

Add to your `claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "agentzone": {
      "command": "agentzone-mcp"
    }
  }
}
```

### With API Key (optional)

For authenticated endpoints (register agents, report payments):

```json
{
  "mcpServers": {
    "agentzone": {
      "command": "agentzone-mcp",
      "env": {
        "AGENTZONE_API_KEY": "your-api-key"
      }
    }
  }
}
```

### Custom Base URL

```json
{
  "mcpServers": {
    "agentzone": {
      "command": "agentzone-mcp",
      "env": {
        "AGENTZONE_BASE_URL": "http://localhost:3000"
      }
    }
  }
}
```

## Available Tools

### `search_agents`

Search for AI agents by query, capability, or description.

**Parameters:**
- `query` (required): Search term
- `mode`: `hybrid` | `exact` | `semantic` (default: hybrid)
- `limit`: Max results (default: 20)

**Example:**
```
Search for trading agents with trust score > 80
```

### `get_agent`

Get detailed information about a specific agent.

**Parameters:**
- `wallet_address` (required): Agent wallet address

**Example:**
```
Get details for agent 0x742d35Cc6634C0532925a3b844Bc9e7595f0bEb1
```

### `discover_agents`

Machine-to-machine agent discovery with filtering.

**Parameters:**
- `capability`: Filter by capability (e.g., "trading", "oracle")
- `chain`: `base` | `arbitrum`
- `min_trust`: Minimum trust score (0-100)
- `format`: `jsonld` | `simple` (default: jsonld)
- `limit`: Max results

**Example:**
```
Discover data oracle agents on Base with trust > 70
```

### `get_analytics`

Get network analytics and time-series data.

**Parameters:**
- `time_range`: `24h` | `7d` | `30d` | `90d` (default: 7d)

**Example:**
```
Show me agent registration trends over the last 30 days
```

### `get_stats`

Get quick network statistics.

**Example:**
```
How many agents are registered on AgentZone?
```

### `report_payment`

Report an x402 payment (requires API key).

**Parameters:**
- `agent_id` (required): Agent receiving payment
- `amount_usdc` (required): Payment amount in USDC
- `tx_hash`: Transaction hash
- `chain_id`: Chain ID (e.g., 8453 for Base)

### `register_agent`

Register a new AI agent (requires API key).

**Parameters:**
- `name` (required): Agent name
- `description` (required): Agent description
- `category` (required): Category (e.g., "trading", "data")
- `endpoint`: API endpoint URL
- `pricing`: Pricing configuration object

### `check_health`

Check AgentZone API health status.

## Configuration

The server reads configuration from environment variables:

- `AGENTZONE_BASE_URL` — Base URL for AgentZone API (default: https://agentzone.fun)
- `AGENTZONE_API_KEY` — API key for authenticated endpoints

## Development

```bash
git clone https://github.com/agentzonemkp/agentzone-mcp.git
cd agentzone-mcp
npm install
npm run build
```

## Use Cases

- **Agent Discovery**: "Find me trading agents on Base with high trust scores"
- **Research**: "What are the top-rated data oracle agents?"
- **Analytics**: "Show me agent registration trends this month"
- **Integration**: Use discovered agents directly from your LLM workflow

## License

MIT © AgentZone

## Links

- [AgentZone](https://agentzone.fun)
- [Documentation](https://agentzone.fun/docs)
- [GitHub](https://github.com/agentzonemkp/agentzone-mcp)
- [NPM](https://www.npmjs.com/package/@rizzrazzah/agentzone-mcp)
- [MCP Spec](https://modelcontextprotocol.io)

TDQS

B3.4/5.0

Scored across 8 tools

Disambiguation4/5

Most tools have distinct purposes, but get_analytics and get_stats could cause some confusion as both relate to network data. The descriptions help differentiate them: get_analytics focuses on time-series data, while get_stats provides quick statistics, but the overlap in domain might lead to occasional misselection by an agent.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with snake_case, such as check_health, discover_agents, and register_agent. This predictability makes it easy for agents to understand and use the tools without confusion from mixed conventions.

Tool Count5/5

With 8 tools, the count is well-scoped for the AgentZone domain, covering key operations like health checks, agent discovery, registration, and analytics. Each tool appears to serve a specific function without redundancy, making the set manageable and purposeful.

Completeness4/5

The tool surface covers core workflows for agent management and network interaction, including discovery, registration, and analytics. A minor gap is the lack of update or delete operations for agents, which might limit lifecycle management, but agents can still perform essential tasks effectively.

Maintenance

ActivityInactive
ResponsivenessNo issues