BCRP Statistical API MCP Server

mcp-bcrp

MCP Server and Python library for the Banco Central de Reserva del Perú (BCRP) Statistical API. Access over 5,000 macroeconomic indicators directly from your AI agent or Python environment.

Overview

The mcp-bcrp package provides a standardized interface to the BCRP statistical database through the Model Context Protocol (MCP). It supports both direct Python usage and integration with AI assistants such as Claude, Gemini, and other MCP-compatible agents.

The library implements:

• Asynchronous HTTP client for efficient data retrieval
• Deterministic search engine with fuzzy matching capabilities
• Spanish language processing for query canonicalization
• Automatic frequency detection (daily, monthly, quarterly, annual)

Features

Requirements

• Python 3.10 or higher
• Internet connection for API requests
• Dependencies: httpx, pandas, fastmcp, rapidfuzz, matplotlib

Installation

From PyPI (when published)

pip install mcp-bcrp

From Source

git clone https://github.com/YOUR_USERNAME/mcp-bcrp.git
cd mcp-bcrp
pip install -e .

With Optional Dependencies

pip install "mcp-bcrp[charts]"  # Include matplotlib for chart generation
pip install "mcp-bcrp[dev]"     # Include development dependencies

Configuration

MCP Server Configuration

Add the following to your MCP configuration file (e.g., mcp_config.json):

{
  "mcpServers": {
    "bcrp-api": {
      "command": "python",
      "args": ["C:/absolute/path/to/mcp_bcrp/run.py"]
    }
  }
}

[!TIP] If you have installed the package via pip, you can also use ["-m", "mcp_bcrp"] as the arguments.

Environment Variables

Usage

As MCP Server

Once configured, the server can be invoked by MCP-compatible AI assistants:

User: What is the current policy interest rate in Peru?
Agent: [calls search_series("tasa politica monetaria")]
Agent: [calls get_data(["PD04722MM"], "2024-01/2025-01")]

As Python Library

import asyncio
from mcp_bcrp.client import AsyncBCRPClient, BCRPMetadata

async def main():
    # Initialize metadata client
    metadata = BCRPMetadata()
    await metadata.load()
    
    # Search for an indicator (deterministic)
    result = metadata.solve("tasa politica monetaria")
    print(result)
    # Output: {'codigo_serie': 'PD04722MM', 'confidence': 1.0, ...}
    
    # Fetch time series data
    client = AsyncBCRPClient()
    df = await client.get_series(
        series_codes=["PD04722MM"],
        start_date="2024-01",
        end_date="2025-01"
    )
    print(df.head())

asyncio.run(main())

Available Tools (MCP)