PancakeSwap PoolSpy MCP Server
# PancakeSwap PoolSpy MCP Server
An MCP server that tracks newly created liquidity pools on Pancake Swap, providing real-time data for DeFi analysts, traders, and developers.
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/)
[](https://smithery.ai/server/@kukapay/pancakeswap-poolspy-mcp)
## Features
- **Real-Time Pool Tracking**: Fetches pools created within a specified time range (default: 5 minutes).
- **Customizable Queries**: Adjust the time range (in seconds) and the number of pools returned (default: 100).
- **Detailed Metrics**: Includes pool address, tokens, creation timestamp, block number, transaction count, volume (USD), and total value locked (USD).
## Prerequisites
- **Python 3.10+**: Ensure Python is installed on your system.
- **The Graph API Key**: Obtain an API key from [The Graph](https://thegraph.com/) to access the PancakeSwap subgraph.
## Installation
### Installing via Smithery
To install PancakeSwap PoolSpy for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@kukapay/pancakeswap-poolspy-mcp):
```bash
npx -y @smithery/cli install @kukapay/pancakeswap-poolspy-mcp --client claude
```
1. **Clone the Repository**:
```bash
git clone https://github.com/kukapay/pancakeswap-poolspy-mcp.git
cd pancakeswap-poolspy-mcp
```
2. **Install Dependencies**:
Install the required Python packages using uv:
```bash
uv add mcp[cli] httpx dotenv
```
3. **Client Configuration**
```json
{
"mcpServers": {
"PancakeSwap-PoolSpy": {
"command": "uv",
"args": ["--directory", "path/to/pancakeswap-poolspy-mcp", "run", "main.py"],
"env": {
"THEGRAPH_API_KEY": "your api key from The Graph"
}
}
}
}
```
## Usage
### Running the Server
Run the server in development mode to test it locally:
```bash
mcp dev main.py
```
This launches the MCP Inspector, where you can interact with the `get_new_pools_bsc` tool.
### Available Tool
#### `get_new_pools_bsc(time_range_seconds: int = 300, limit: int = 100)`
Fetches a list of newly created PancakeSwap pools on BNB Smart Chain.
- **Parameters**:
- `time_range_seconds` (int): Time range in seconds to look back for new pools. Default is 300 seconds (5 minutes).
- `limit` (int): Maximum number of pools to return. Default is 100 pools.
- **Returns**: A formatted string listing pool details or an error message if the query fails.
- **Example Outputs**:
- Default (last 5 minutes, up to 100 pools):
```bash
get_new_pools_bsc()
```
```
Newly Created Trading Pools (Last 5 Minutes, Limit: 100):
Pool Address: 0x1234...5678
Tokens: WETH/USDC
Created At: 2025-03-16 12:00:00 UTC
Block Number: 12345678
Transaction Count: 10
Volume (USD): 1234.56
Total Value Locked (USD): 5678.90
Pool Address: 0x9abc...def0
Tokens: CAKE/BNB
Created At: 2025-03-16 12:01:00 UTC
Block Number: 12345679
Transaction Count: 5
Volume (USD): 789.12
Total Value Locked (USD): 3456.78
```
- Custom (last 10 minutes, up to 50 pools):
```bash
get_new_pools(600, 50)
```
```
Newly Created Trading Pools (Last 10 Minutes, Limit: 50):
[pool details...]
```
### **Example Prompts**:
- "list newly created PancakeSwap pools from the last 1 hours."
- "Display PancakeSwap pools created within the last 2 minutes."
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and distinct by default.
A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'get_new_pools_bsc' follows a clear verb_noun_domain pattern.
One tool is too few for a server focused on monitoring PancakeSwap pools, as it lacks essential operations like retrieving pool details, tracking liquidity changes, or analyzing historical data. This severely limits the server's utility for comprehensive agent workflows.
The tool surface is severely incomplete for the PancakeSwap monitoring domain. While it covers new pool discovery, it lacks tools for querying existing pools, checking pool metrics (e.g., volume, fees), or performing deeper analysis, leaving significant gaps that will hinder agent tasks.