🔌 ¡ Compatible con Cline, Cursor, Claude Desktop y cualquier otro cliente MCP!
El detector de pared de volumen MCP funciona a la perfección con cualquier cliente MCP
El Protocolo de Contexto de Modelo (MCP) es un estándar abierto que permite que los sistemas de IA interactúen sin problemas con diversas fuentes de datos y herramientas, lo que facilita conexiones bidireccionales seguras.
El servidor MCP del detector de pared de volumen proporciona:
Análisis del volumen de negociación de acciones en tiempo real
Detección de niveles de precios significativos (muros de volumen)
Seguimiento y análisis de desequilibrios comerciales
Análisis de operaciones fuera de horario
Persistencia de datos basada en MongoDB
Antes de comenzar, asegúrese de tener:
Instancia de MongoDB en ejecución
Acceso a la API del mercado de valores
Node.js (v20 o superior)
Git instalado (solo es necesario si se utiliza el método de instalación Git)
Related MCP server: crypto-orderbook-mcp
npx -y volume-wall-detector-mcp@latestPara instalar Volume Wall Detector MCP Server para Claude Desktop automáticamente a través de Smithery:
npx -y @smithery/cli install volume-wall-detector-mcp --client claudeAbra el archivo de configuración de Cline MCP:
# 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.jsonAgregue la configuración del servidor Volume Wall Detector:
{
"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": []
}
}
}Nota : Requiere la versión de Cursor 0.45.6 o superior
Abrir configuración del cursor
Navegar hasta Abrir MCP
Haga clic en "Agregar nuevo servidor MCP global"
Complete la siguiente información:
Nombre : "detector de volumen de pared mcp"
Tipo : "comando"
Comando : GXP5
Cree o edite el archivo de configuración de Claude Desktop:
code "$HOME/Library/Application Support/Claude/claude_desktop_config.json"code %APPDATA%\Claude\claude_desktop_config.jsonAñade la configuración:
{
"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"
}
}
}
}Instituto Tecnológico de Massachusetts (MIT)
Analyze stock data including volume and value analysis
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Number of days to analyze (optional) | |
| symbol | Yes | Stock symbol to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'analyze' but doesn't specify whether this is a read-only operation, requires authentication, has rate limits, or what the output format might be. The description is vague about behavioral traits, failing to compensate for the lack of annotations.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, though it could be slightly more structured by separating key points. Every sentence earns its place, but there's room for minor improvement in clarity.
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 of stock analysis, lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'volume and value analysis' entails, the return values, or any behavioral constraints. The description fails to provide sufficient context for an agent to understand the tool's full scope and usage.
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?
The schema description coverage is 100%, with clear descriptions for both parameters ('days' and 'symbol') in the input schema. The description adds no additional meaning beyond what the schema provides, such as explaining how 'volume and value analysis' relates to these parameters. Baseline score of 3 is appropriate as the schema does the heavy lifting.
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 tool's purpose as analyzing stock data with specific mention of 'volume and value analysis', providing a verb ('analyze') and resource ('stock data'). However, it doesn't explicitly differentiate from sibling tools like 'fetch-order-book' or 'fetch-trades', which might also involve stock data analysis but with different focuses or methods.
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 provides no guidance on when to use this tool versus the sibling tools ('fetch-order-book' and 'fetch-trades'), nor does it mention any prerequisites, exclusions, or alternative contexts. It lacks explicit usage instructions, leaving the agent to infer based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch current order book data for a symbol
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Stock symbol to fetch order book for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool fetches data, implying a read-only operation, but doesn't specify critical details like rate limits, authentication requirements, data freshness, or error handling. This leaves significant gaps in understanding the tool's behavior.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core action and resource, making it highly concise and well-structured for quick comprehension.
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 lack of annotations and output schema, the description is insufficiently complete. It doesn't explain what the order book data includes (e.g., bid/ask prices, depths), how it's formatted, or any limitations, leaving the agent with incomplete context for effective use.
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?
The input schema has 100% description coverage, with the 'symbol' parameter clearly documented as 'Stock symbol to fetch order book for'. The description adds no additional parameter semantics beyond this, so it meets the baseline for high schema coverage without compensating value.
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 action ('fetch') and resource ('current order book data for a symbol'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'analyze-stock' or 'fetch-trades', which might also involve stock data retrieval, preventing a perfect score.
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 provides no guidance on when to use this tool versus alternatives like 'analyze-stock' or 'fetch-trades'. It lacks context about specific use cases, prerequisites, or exclusions, leaving the agent to infer usage based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch recent trades for a symbol
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | Stock symbol to fetch trades for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('fetch') but doesn't describe traits like whether this is a read-only operation, potential rate limits, authentication needs, or what 'recent' means (e.g., time range, pagination). This leaves significant gaps for a tool that likely queries external data.
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 a single, efficient sentence with zero waste—it directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple tool and front-loaded with the core action.
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 no annotations and no output schema, the description is incomplete for a tool that fetches data. It doesn't explain what 'recent' entails, the return format, or any behavioral context, which are critical for an agent to use it correctly. The simplicity of the tool doesn't compensate for these omissions.
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?
The input schema has 100% description coverage, with the 'symbol' parameter clearly documented. The description adds no additional meaning beyond what the schema provides (e.g., it doesn't clarify format examples or constraints), so it meets the baseline of 3 where the schema does the heavy lifting.
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 action ('fetch') and target ('recent trades for a symbol'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'analyze-stock' or 'fetch-order-book', which might also involve symbol data, so it misses full sibling distinction.
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 provides no guidance on when to use this tool versus alternatives like 'analyze-stock' or 'fetch-order-book'. It implies usage for recent trades but offers no exclusions, prerequisites, or context about when it's appropriate, leaving the agent to guess based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0analyze-stockfetch-order-bookfetch-tradesEach tool has a clearly distinct purpose: analyze-stock focuses on data analysis, fetch-order-book retrieves order book data, and fetch-trades gets trade history. There is no overlap in functionality, making it easy for an agent to select the right tool without confusion.
The naming is mostly consistent with a verb-noun pattern (analyze-stock, fetch-order-book, fetch-trades), using kebab-case throughout. The minor deviation is that analyze-stock uses 'analyze' while the others use 'fetch', but this is reasonable given the different actions.
With only 3 tools, the set feels thin for a server named 'Volume Wall Detector MCP', which suggests a focus on volume analysis in trading. While the tools cover basic data fetching and analysis, more tools might be expected for comprehensive volume detection or trading operations.
The tools provide core data retrieval (order book, trades) and analysis, but there are notable gaps for a volume-focused detector, such as tools for real-time volume alerts, historical volume trends, or integration with trading actions. The surface is functional but incomplete for advanced volume analysis workflows.
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