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MCP Server for Banco Central de Reserva del Perú (BCRP) Statistical API

Project description

mcp-bcrp

Python Version GitHub PyPI License: MIT

User Guide Colab

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.


Table of Contents


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

Feature Description
Smart Search Deterministic search engine with fuzzy matching, attribute extraction, and ambiguity detection
Async Native Built on httpx for non-blocking HTTP requests with connection pooling
Dual Interface Use as MCP server for AI agents or as standalone Python library
Chart Generation Generate publication-ready charts with automatic Spanish date parsing
Full Coverage Access to 5,000+ BCRP economic indicators across all categories
Metadata Cache Local caching of 17MB metadata file for fast offline searches

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

Variable Description Default
BCRP_CACHE_DIR Directory for metadata cache User cache dir
BCRP_TIMEOUT HTTP request timeout in seconds 120

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)

Tool Parameters Description
search_series query: str Search BCRP indicators by keyword. Returns deterministic match or ambiguity error.
get_data series_codes: list[str], period: str Fetch raw time series data. Period format: YYYY-MM/YYYY-MM.
get_table series_codes: list[str], names: list[str], period: str Get formatted table with optional custom column names.
plot_chart series_codes: list[str], period: str, title: str, names: list[str], output_path: str Generate professional PNG chart with automatic date parsing.

Available Resources

Resource URI Description
Metadata bcrp://metadata Summary of cached metadata and stats.
Key Indicators bcrp://indicators/key List of most common economic indicators.
Help bcrp://help Usage guide and tips.

Available Prompts

Prompt Description
economista_peruano System prompt to analyze data as a BCRP Senior Economist with rigorous methodology.
analista_financiero Expert persona for short-term financial market analysis (Forex, Rates).
explorador_datos Assistant to help find the correct series codes for a given topic.

Key Indicators

The following are commonly used indicator codes:

Category Code Description Frequency
Monetary Policy PD04722MM Reference Interest Rate Monthly
Exchange Rate PD04638PD Interbank Exchange Rate (Sell) Daily
Inflation PN01270PM CPI Lima Metropolitan Monthly
Copper Price PN01652XM International Copper Price (c/lb) Monthly
GDP Growth PN01713AM Agricultural GDP (Var. %) Annual
Business Expectations PD38048AM GDP Expectations 12 months Monthly
International Reserves PN00015MM Net International Reserves Monthly

[!NOTE] Series codes follow the BCRP naming convention. Use search_series to find the appropriate code for your query.


Search Engine

The search engine implements a deterministic pipeline designed for high precision:

Query Input
    │
    ▼
┌─────────────────────────────┐
│  1. Canonicalization        │  Lowercase, remove accents, filter stopwords
└─────────────────────────────┘
    │
    ▼
┌─────────────────────────────┐
│  2. Attribute Extraction    │  Currency (USD/PEN), horizon, component type
└─────────────────────────────┘
    │
    ▼
┌─────────────────────────────┐
│  3. Hard Filters            │  Eliminate series not matching attributes
└─────────────────────────────┘
    │
    ▼
┌─────────────────────────────┐
│  4. Fuzzy Scoring           │  Token sort ratio using RapidFuzz
└─────────────────────────────┘
    │
    ▼
┌─────────────────────────────┐
│  5. Ambiguity Detection     │  Return error if top matches are too close
└─────────────────────────────┘
    │
    ▼
Deterministic Result or Explicit Ambiguity Error

Architecture

mcp_bcrp/
├── __init__.py          # Package initialization and version
├── server.py            # FastMCP server with tool definitions
├── client.py            # AsyncBCRPClient and BCRPMetadata classes
└── search_engine.py     # Deterministic search pipeline implementation

run.py                   # MCP server entry point
bcrp_metadata.json       # Cached metadata (17MB, auto-downloaded)

Limitations and Warnings

[!WARNING] API Rate Limits: The BCRP API does not publish official rate limits. Implement appropriate delays between requests in production applications to avoid IP blocking.

[!WARNING] Data Freshness: Metadata cache (bcrp_metadata.json) may become stale. Delete the file periodically to force a refresh of available indicators.

[!CAUTION] Unofficial Package: This is an independent implementation and is not officially endorsed by the Banco Central de Reserva del Peru. Data accuracy depends on the upstream API.

Known Limitations

  1. Date Format: The BCRP API returns dates in Spanish format (e.g., "Ene.2024"). The library handles this automatically, but custom date parsing may be required for edge cases.

  2. Series Availability: Not all series are available for all time periods. The API returns empty responses for unavailable date ranges.

  3. Metadata Size: The complete metadata file is approximately 17MB. Initial load may take several seconds on slow connections.

  4. Frequency Detection: The library attempts to auto-detect series frequency, but some series may require explicit specification.


Contributing

Contributions are welcome. Please follow these guidelines:

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/improvement)
  3. Commit changes with descriptive messages
  4. Ensure all tests pass (pytest)
  5. Submit a pull request

See CONTRIBUTING.md for detailed guidelines.


License

This project is licensed under the MIT License. See LICENSE for the full text.


Acknowledgments


See Also

Project Description
wbgapi360 Enterprise-grade MCP Client for World Bank Data API. Provides access to World Development Indicators, global rankings, country comparisons, and professional FT-style visualizations.

Both libraries can be used together to build comprehensive macroeconomic analysis pipelines combining Peru-specific BCRP data with global World Bank indicators.


Disclaimer: This software is provided "as is" without warranty of any kind. The authors are not responsible for any errors in the data or any decisions made based on the information provided by this library.

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