mcp-bcrp 0.1.3

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

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.

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Table of Contents

• Overview
• Features
• Requirements
• Installation
• Configuration
• Usage
  • MCP Server
  • Python Library
• Available Tools
• Key Indicators
• Search Engine
• Architecture
• Limitations and Warnings
• Contributing
• License
• Acknowledgments

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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)

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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

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Requirements

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

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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

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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

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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())

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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 Prompts

Prompt	Description
economista_peruano	System prompt to analyze data as a BCRP Senior Economist with rigorous methodology

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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.

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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

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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)

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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.

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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.

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License

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

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Acknowledgments

• Banco Central de Reserva del Peru for providing the public statistical API
• FastMCP for the Model Context Protocol framework
• RapidFuzz for fuzzy string matching
• usebcrp for inspiration on BCRP API integration

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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.

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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.