🔌 Cline、Cursor、Claude Desktop、その他の MCP クライアントと互換性があります。
Volume Wall Detector MCPは、あらゆるMCPクライアントとシームレスに連携します。
モデル コンテキスト プロトコル (MCP) は、AI システムがさまざまなデータ ソースやツールとシームレスに対話し、安全な双方向接続を実現できるようにするオープン スタンダードです。
Volume Wall Detector MCP サーバーは以下を提供します。
リアルタイムの株式取引量分析
重要な価格レベルの検出（ボリュームウォール）
取引不均衡の追跡と分析
時間外取引分析
MongoDBベースのデータ永続性
始める前に、次のものを用意してください。
MongoDBインスタンスが実行中
株式市場APIアクセス
Node.js (v20以上)
Git がインストールされている (Git インストール方法を使用する場合のみ必要)
Related MCP server: crypto-orderbook-mcp
npx -y volume-wall-detector-mcp@latestSmithery 経由で Claude Desktop 用の Volume Wall Detector MCP Server を自動的にインストールするには:
npx -y @smithery/cli install volume-wall-detector-mcp --client claudeCline MCP 設定ファイルを開きます。
# 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.jsonVolume Wall Detector サーバー構成を追加します。
{
"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": []
}
}
}注: カーソルバージョン0.45.6以上が必要です
カーソル設定を開く
MCPを開く
「新しいグローバルMCPサーバーを追加」をクリックします。
以下の情報を入力してください。
名前: 「volume-wall-detector-mcp」
タイプ：「コマンド」
コマンド: GXP5
Claude Desktop 構成ファイルを作成または編集します。
code "$HOME/Library/Application Support/Claude/claude_desktop_config.json"code %APPDATA%\Claude\claude_desktop_config.json設定を追加します:
{
"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"
}
}
}
}マサチューセッツ工科大学
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.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
MCP server with quote and live cryptocurrency price tools, local and cloud-deployed transports.
MCP server for OpenMM — exposes market data, account, trading, and strategy tools to AI agents
MCP server for Gainium — manage trading bots, deals, and balances via AI assistants
Unlock the power of real-time cryptocurrency data with our Crypto Price Insights MCP server.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/Cognitive-Stack/volume-wall-detector-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
