PancakeSwap PoolSpy MCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@PancakeSwap PoolSpy MCP Servershow me new pools from the last 10 minutes"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
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).
Related MCP server: uniswap-poolspy-mcp
Prerequisites
Python 3.10+: Ensure Python is installed on your system.
The Graph API Key: Obtain an API key from The Graph to access the PancakeSwap subgraph.
Installation
Installing via Smithery
To install PancakeSwap PoolSpy for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @kukapay/pancakeswap-poolspy-mcp --client claudeClone the Repository:
git clone https://github.com/kukapay/pancakeswap-poolspy-mcp.git cd pancakeswap-poolspy-mcpInstall Dependencies: Install the required Python packages using uv:
uv add mcp[cli] httpx dotenvClient Configuration
{ "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:
mcp dev main.pyThis 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):
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): 3456.78Custom (last 10 minutes, up to 50 pools):
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 file for details.
Available Tools
1 toolget_new_pools_bscA
Returns a list of trading pools created in the specified time range on Pancake Swap V3 BNB Smart Chain.
Parameters: time_range_seconds (int): The time range in seconds to look back for new pools. Default is 300 seconds (5 minutes). limit (int): The maximum number of pools to return. Default is 100 pools.
| Name | Required | Description | Default |
|---|---|---|---|
| time_range_seconds | No | ||
| limit | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes the retrieval behavior but lacks details on potential side effects (e.g., rate limits, authentication needs, data freshness, or error handling). While it specifies the scope (new pools in a time range), it does not disclose behavioral traits like pagination, sorting, or what happens if no pools are found, leaving gaps for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by a structured parameter list with clear explanations and defaults. Every sentence adds value without redundancy, making it efficient and easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a read operation with two parameters), no annotations, and no output schema, the description is moderately complete. It covers the purpose and parameters well but lacks details on return values (e.g., pool structure, fields) and behavioral aspects like error handling or performance. This leaves some gaps for an agent to use the tool effectively without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics by explaining that 'time_range_seconds' is 'the time range in seconds to look back for new pools' with a default, and 'limit' is 'the maximum number of pools to return' with a default. This clarifies the purpose and usage of both parameters beyond what the schema provides, though it could include more on constraints (e.g., min/max values).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Returns a list'), resource ('trading pools'), and scope ('created in the specified time range on Pancake Swap V3 BNB Smart Chain'). It distinguishes this as a retrieval operation for newly created pools with temporal filtering, making the purpose immediately understandable without redundancy.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying the time range for new pools, but it does not provide explicit guidance on when to use this tool versus alternatives (e.g., for historical vs. real-time data, or other filtering criteria). Since no sibling tools are listed, the lack of comparative guidance is less critical, but it still lacks explicit when/when-not directives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.0- Changed
get_new_pools_bsc1 field changed- added
Input schema / titleAdded value: +"get_new_pools_bscArguments"
1 tool update
- First observed
get_new_pools_bsc
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.
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