by cognitive-stack
Volume Wall Detector analyzes trading data to spot volume walls and key price levels for better market insights and pote
Analyzes stock trading volume data to identify volume walls and significant price levels where heavy trading activity occurs. Helps traders spot potential market reversal points and understand market structure.
Volume Wall Detector is a community-built MCP server published by cognitive-stack that provides AI assistants with tools and capabilities via the Model Context Protocol. Volume Wall Detector analyzes trading data to spot volume walls and key price levels for better market insights and pote It is categorized under finance, analytics data.
You can install Volume Wall Detector in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Volume Wall Detector is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
We evaluated Volume Wall Detector against two servers with overlapping tools; this profile had the clearer scope statement.
Volume Wall Detector has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Volume Wall Detector is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
According to our notes, Volume Wall Detector benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
According to our notes, Volume Wall Detector benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Volume Wall Detector has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
We evaluated Volume Wall Detector against two servers with overlapping tools; this profile had the clearer scope statement.
We wired Volume Wall Detector into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Volume Wall Detector is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Volume Wall Detector has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
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<p align="center"> <img src="vld-logo.png" width="300" alt="VLD Logo"> </p>🔌 Compatible with Cline, Cursor, Claude Desktop, and any other MCP Clients!
Volume Wall Detector MCP works seamlessly with any MCP client
The Model Context Protocol (MCP) is an open standard that enables AI systems to interact seamlessly with various data sources and tools, facilitating secure, two-way connections.
The Volume Wall Detector MCP server provides:
Before you begin, ensure you have:
npx -y volume-wall-detector-mcp@latest
To install Volume Wall Detector MCP Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install volume-wall-detector-mcp --client claude
# For macOS:
code ~/Library/Application\ Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
# For Windows:
code %APPDATA%\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json
{
"mcpServers": {
"volume-wall-detector-mcp": {
"command": "npx",
"args": ["-y", "volume-wall-detector-mcp@latest"],
"env": {
"TIMEZONE": "GMT+7",
"API_BASE_URL": "your-api-url-here",
"MONGO_HOST": "localhost",
"MONGO_PORT": "27017",
"MONGO_DATABASE": "volume_wall_detector",
"MONGO_USER": "admin",
"MONGO_PASSWORD": "password",
"MONGO_AUTH_SOURCE": "admin",
"MONGO_AUTH_MECHANISM": "SCRAM-SHA-1",
"PAGE_SIZE": "50",
"TRADES_TO_FETCH": "10000",
"DAYS_TO_FETCH": "1",
"TRANSPORT_TYPE": "stdio",
"PORT": "8080"
},
"disabled": false,
"autoApprove": []
}
}
}
Note: Requires Cursor version 0.45.6 or higher
env TIMEZONE=GMT+7 API_BASE_URL=your-api-url-here MONGO_HOST=localhost MONGO_PORT=27017 MONGO_DATABASE=volume_wall_detector MONGO_USER=admin MONGO_PASSWORD=password MONGO_AUTH_SOURCE=admin MONGO_AUTH_MECHANISM=SCRAM-SHA-1 PAGE_SIZE=50 TRADES_TO_FETCH=10000 DAYS_TO_FETCH=1 npx -y volume-wall-detector-mcp@latest
Create or edit the Claude Desktop configuration file:
code "$HOME/Library/Application Support/Claude/claude_desktop_config.json"
code %APPDATA%\Claude\claude_desktop_config.json
Add the configuration:
{
"mcpServers": {
"volume-wall-detector-mcp": {
"command": "npx",
"args": ["-y", "volume-wall-detector-mcp@latest"],
"env": {
"TIMEZONE": "GMT+7",
"API_BASE_URL": "your-api-url-here",
"MONGO_HOST": "localhost",
"MONGO_PORT": "27017",
"MONGO_DATABASE": "volume_wall_detector",
"MONGO_USER": "admin",
"MONGO_PASSWORD": "password",
"MONGO_AUTH_SOURCE": "admin",
"MONGO_AUTH_MECHANISM": "SCRAM-SHA-1",
"PAGE_SIZE": "50",
"TRADES_TO_FETCH": "10000",
"DAYS_TO_FETCH": "1",
"TRANSPORT_TYPE": "stdio",
"PORT": "8080"
}
}
}
}
MIT
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.