VIMO MCP Server: Claude Access to Vietnam Stocks in 5 Mins

Each tool definition within the VIMO MCP Server is meticulously structured using JSON Schema, providing Claude with a precise understanding of its capabilities, required parameters, and expected output. This level of detail is crucial for enabling the LLM to intelligently select and execute the most appropriate tool for a given query, enhancing the accuracy and relevance of the generated insights. For example, a tool might define parameters for `ticker`, `start_date`, `end_date`, and `analysis_type`, along with a natural language description that helps Claude understand its purpose.

Here is a simplified conceptual representation of how a VIMO MCP tool, such as get_stock_analysis, might be defined for consumption by an LLM:

{
  "name": "get_stock_analysis",
  "description": "Retrieves comprehensive analysis for a given stock ticker, including fundamentals, technicals, and sentiment.",
  "input_schema": {
    "type": "object",
    "properties": {
      "ticker": {
        "type": "string",
        "description": "The stock ticker symbol (e.g., 'FPT', 'HPG')."
      },
      "date_range": {
        "type": "string",
        "enum": ["1D", "5D", "1M", "3M", "6M", "1Y"],
        "description": "The historical data range for analysis."
      },
      "include_news": {
        "type": "boolean",
        "description": "Whether to include recent news articles in the analysis (default: false)."
      }
    },
    "required": ["ticker", "date_range"]
  },
  "output_schema": {
    "type": "object",
    "properties": {
      "ticker": {"type": "string"},
      "price": {"type": "number"},
      "pe_ratio": {"type": "number"},
      "eps": {"type": "number"},
      "volume": {"type": "number"},
      "technical_summary": {"type": "string"},
      "sentiment_score": {"type": "number"},
      "news_summary": {"type": "array", "items": {"type": "string"}}
    }
  }
}

This structured approach ensures that when Claude receives a query like, "Analyze FPT's performance over the last month, including any relevant news," it can confidently identify `get_stock_analysis` as the appropriate tool, extract the necessary parameters (`ticker: 'FPT'`, `date_range: '1M'`, `include_news: true`), and then invoke the tool via the VIMO MCP Server. The server handles the underlying data retrieval, aggregation, and formatting, returning a clean, structured result for Claude to synthesize into a coherent response.

Architecting Claude for Vietnam Stock Intelligence

Integrating Claude with VIMO MCP Server for Vietnam stock intelligence transforms it from a general-purpose LLM into a specialized financial analyst. The architecture revolves around Claude's ability to reason about and utilize external tools, a feature increasingly sophisticated in modern LLMs. The process begins with providing Claude with the definitions of the VIMO MCP tools. Once these definitions are established, Claude can receive natural language queries, parse them, identify the need for specific tools, construct the appropriate tool calls based on the provided schemas, and then process the results to formulate comprehensive answers.

A typical interaction flow would involve these steps:

This iterative process allows Claude to perform complex multi-step reasoning, chaining together various financial tools to answer nuanced questions that would be impossible for an LLM to address with only its pre-trained knowledge or generic search capabilities. The key to successful implementation lies in effective prompt engineering, not just for the initial query, but also in how the tool definitions are presented to Claude to optimize its selection and parameter extraction.

Here is an example of how a Claude API call might look when invoking a VIMO MCP tool, leveraging Claude's `tools` and `tool_use` capabilities (simplified for illustration):

import Anthropic from '@anthropic-ai/sdk';

const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });

async function queryClaudeWithVimoMCP(userQuery: string) {
  const tools = [
    {
      "name": "get_stock_analysis",
      "description": "Retrieves comprehensive analysis for a given stock ticker, including fundamentals, technicals, and sentiment.",
      "input_schema": {
        "type": "object",
        "properties": {
          "ticker": { "type": "string", "description": "The stock ticker symbol (e.g., 'FPT', 'HPG')." },
          "date_range": { "type": "string", "enum": ["1D", "5D", "1M", "3M", "6M", "1Y"], "description": "The historical data range for analysis." },
          "include_news": { "type": "boolean", "description": "Whether to include recent news articles in the analysis (default: false)." }
        },
        "required": ["ticker", "date_range"]
      }
    },
    {
      "name": "get_financial_statements",
      "description": "Fetches a company's financial statements (Balance Sheet, Income Statement, Cash Flow) for a specified period.",
      "input_schema": {
        "type": "object",
        "properties": {
          "ticker": { "type": "string", "description": "The stock ticker symbol (e.g., 'FPT', 'HPG')." },
          "statement_type": { "type": "string", "enum": ["balance_sheet", "income_statement", "cash_flow"], "description": "Type of financial statement to retrieve." },
          "period": { "type": "string", "enum": ["quarterly", "annually"], "description": "Reporting period (quarterly or annually)." },
          "year": { "type": "number", "description": "The specific year for the statement (e.g., 2023)." }
        },
        "required": ["ticker", "statement_type", "period", "year"]
      }
    }
    // ... other VIMO MCP tool definitions
  ];

  const response = await anthropic.messages.create({
    model: "claude-3-opus-20240229", // Or other Claude 3 models
    max_tokens: 2000,
    tools: tools,
    messages: [
      {
        role: "user",
        content: userQuery,
      },
    ],
  });

  console.log(JSON.stringify(response, null, 2));

  // Handle tool_use in the response
  for (const contentBlock of response.content) {
    if (contentBlock.type === 'tool_use') {
      const toolName = contentBlock.name;
      const toolInput = contentBlock.input;

      console.log(`Claude wants to use tool: ${toolName} with input:`, toolInput);
      // In a real application, you would now call the VIMO MCP Server
      // and then provide the tool_result back to Claude in a subsequent message.
    } else if (contentBlock.type === 'text') {
      console.log(`Claude's direct response: ${contentBlock.text}`);
    }
  }
}

queryClaudeWithVimoMCP("Tell me FPT's latest P/E ratio and recent volume. Also, what was its revenue in 2023's Q4?");

This TypeScript example illustrates how tool definitions are provided to Claude. When Claude identifies a relevant tool, it will respond with a `tool_use` content block, prompting the developer's application to execute that tool via the VIMO MCP Server. The results are then fed back to Claude, allowing for a conversational and data-driven analytical process. The VIMO MCP Server handles the crucial backend work of securely accessing and processing the requested Vietnam stock market data, returning it in a format Claude can readily consume.

How to Get Started: Integrating VIMO MCP with Your Claude Agent

Integrating VIMO MCP Server into your Claude-powered AI agent to access Vietnam stock data is designed to be a streamlined process, minimizing setup time and maximizing development velocity. The 2026 update to VIMO MCP Server focuses on enhanced API stability, expanded tool definitions, and improved documentation, ensuring a smooth developer experience. Here’s a step-by-step guide to get you operational quickly:

Step 1: Sign Up for VIMO MCP Server Access

Begin by registering for an account on the VIMO MCP Server portal. This process typically involves providing basic contact information and agreeing to the terms of service. Upon successful registration, you will gain access to your personalized dashboard, which includes your unique API key.

Step 2: Obtain Your VIMO MCP API Key

Your API key is essential for authenticating requests to the VIMO MCP Server. Navigate to the 'API Keys' section within your VIMO dashboard. Securely store this key, as it provides programmatic access to all the available financial tools. It is recommended to use environment variables for managing API keys in your application to prevent their accidental exposure in source code.

Step 3: Familiarize Yourself with VIMO MCP Tool Definitions

Explore the comprehensive list of available tools on the VIMO MCP Server documentation. Each tool, such as get_stock_analysis, get_market_overview, or get_foreign_flow, comes with a detailed JSON Schema defining its `name`, `description`, `input_schema`, and `output_schema`. Understanding these schemas is critical for correctly configuring Claude's tool-use capabilities. The VIMO platform provides examples for each tool, demonstrating expected inputs and outputs.

Step 4: Configure Claude with VIMO MCP Tool Definitions

The core of the integration lies in providing Claude with the definitions of the VIMO MCP tools. This is done through the `tools` parameter in your Claude API calls. You will essentially copy the JSON Schema definitions of the VIMO MCP tools you intend to use and pass them to Claude. This allows Claude to understand the capabilities and parameters of each tool. For a robust agent, you would register all relevant VIMO MCP tools.

import Anthropic from '@anthropic-ai/sdk';
import axios from 'axios'; // For making HTTP calls to VIMO MCP Server

const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
const VIMO_MCP_API_BASE_URL = "https://api.vimo.cuthongthai.vn/mcp";
const VIMO_MCP_API_KEY = process.env.VIMO_MCP_API_KEY;

// Example of dynamically fetching tool definitions (recommended for larger toolsets)
async function getVimoMcpTools() {
    // In a real scenario, you'd fetch this from a VIMO API endpoint
    // or load from a local configuration file if static.
    // For this example, we'll hardcode a few tools.
    return [
        {
            "name": "get_stock_analysis",
            "description": "Retrieves comprehensive analysis for a given stock ticker, including fundamentals, technicals, and sentiment.",
            "input_schema": {
                "type": "object",
                "properties": {
                    "ticker": { "type": "string", "description": "The stock ticker symbol (e.g., 'FPT', 'HPG')." },
                    "date_range": { "type": "string", "enum": ["1D", "5D", "1M", "3M", "6M", "1Y"], "description": "The historical data range for analysis." },
                    "include_news": { "type": "boolean", "description": "Whether to include recent news articles in the analysis (default: false)." }
                },
                "required": ["ticker", "date_range"]
            }
        },
        {
            "name": "get_foreign_flow",
            "description": "Fetches foreign investor trading activity (buy/sell volume and value) for a specific stock or the entire market.",
            "input_schema": {
                "type": "object",
                "properties": {
                    "ticker": { "type": "string", "description": "Optional: Stock ticker symbol (e.g., 'FPT'). If omitted, returns market-wide foreign flow." },
                    "period": { "type": "string", "enum": ["today", "5D", "1M"], "description": "The period for foreign flow data." }
                },
                "required": ["period"]
            }
        }
        // ... more tools from VIMO MCP Server
    ];
}

async function runClaudeAgent(userQuery: string) {
  const vimoTools = await getVimoMcpTools();

  let conversationHistory: Anthropic.Messages.Message[] = [{
    role: "user",
    content: userQuery,
  }];

  let response = await anthropic.messages.create({
    model: "claude-3-opus-20240229",
    max_tokens: 4000,
    tools: vimoTools,
    messages: conversationHistory,
  });

  // Loop to handle potential multi-turn tool use
  while (response.stop_reason === 'tool_use') {
    conversationHistory.push(response.content[0] as Anthropic.Messages.Message); // Add Claude's tool_use to history

    for (const contentBlock of response.content) {
      if (contentBlock.type === 'tool_use') {
        const toolName = contentBlock.name;
        const toolInput = contentBlock.input;

        console.log(`Claude wants to use tool: ${toolName} with input:`, toolInput);

        let toolResult;
        try {
          // *** Crucial step: Call VIMO MCP Server with the tool details ***
          const vimoResponse = await axios.post(
            `${VIMO_MCP_API_BASE_URL}/execute`, // Assuming a generic execute endpoint
            { tool_name: toolName, ...toolInput },
            { headers: { 'Authorization': `Bearer ${VIMO_MCP_API_KEY}` } }
          );
          toolResult = vimoResponse.data;
          console.log(`VIMO MCP Tool result for ${toolName}:`, toolResult);
        } catch (error: any) {
          console.error(`Error executing VIMO MCP tool ${toolName}:`, error.message);
          toolResult = { error: `Failed to execute tool ${toolName}: ${error.message}` };
        }

        // Add the tool_result back to the conversation history for Claude
        conversationHistory.push({
          role: "user",
          content: [{
            type: "tool_result",
            tool_use_id: contentBlock.id,
            content: JSON.stringify(toolResult),
          }],
        });
      }
    }

    // Get Claude's next response after providing tool results
    response = await anthropic.messages.create({
      model: "claude-3-opus-20240229",
      max_tokens: 4000,
      tools: vimoTools,
      messages: conversationHistory,
    });
  }

  // Final response from Claude
  console.log("Final Claude Response:", response.content[0].text);
  return response.content[0].text;
}

runClaudeAgent("What are the key technical indicators for FPT over the last 3 months, and what's the recent foreign trading sentiment for it?");

Step 5: Test and Iterate

With your Claude agent configured, send test queries. Monitor Claude's responses to ensure it correctly identifies and utilizes the VIMO MCP tools. Pay close attention to the `tool_use` blocks in Claude's output and verify that the parameters are correctly extracted and passed to your VIMO MCP Server execution logic. Refine your prompt engineering and potentially adjust tool descriptions as needed to optimize performance. The VIMO platform also offers AI Stock Screener, which utilizes these underlying MCP tools to help validate your integration and understanding of the data.

This structured approach ensures that you can rapidly deploy a sophisticated AI agent capable of providing deep insights into the Vietnam stock market, significantly accelerating your development cycle and enhancing the analytical capabilities of your financial applications.

Conclusion: Unlocking Advanced AI Financial Intelligence

The integration of advanced LLMs like Claude with real-time, context-rich financial data from markets such as Vietnam represents a critical frontier in AI-driven finance. Traditional integration methods, characterized by their high complexity and fragility, have historically hindered the development of truly intelligent financial agents. The Model Context Protocol (MCP), particularly through VIMO MCP Server, fundamentally transforms this landscape by providing a standardized, robust, and developer-friendly framework for tool orchestration and data access.

By leveraging VIMO MCP Server's specialized toolkit, developers can drastically reduce the time and effort required to connect Claude to granular Vietnam stock market data. The N×M integration problem is elegantly resolved, enabling a 1×1 interaction where Claude can confidently identify, invoke, and interpret results from a suite of carefully curated financial analysis tools. This not only accelerates development but also significantly enhances the reliability and contextual accuracy of the AI's outputs, moving beyond mere data retrieval to genuine financial intelligence.

The 2026 update to VIMO MCP Server reinforces its commitment to providing cutting-edge capabilities, ensuring that developers have access to the most current and comprehensive data, along with a robust protocol designed for future scalability. For quantitative analysts, AI researchers, and developers looking to build sophisticated financial applications for the Vietnamese market, VIMO MCP Server offers an unparalleled advantage.

🤖 VIMO Research Note: The ability of LLMs to self-correct and learn from tool feedback loops, facilitated by MCP, is driving a new era of autonomous financial agents, capable of adapting to market dynamics in real-time.