AgentZone MCP
# @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
Scored across 8 tools
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.
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.
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.
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.