Investment Statement MCP Server

Parse and query investment statements from multiple institutions using natural language.

Features

• Multi-Institution Support: Currently supports Questrade (Interactive Brokers and Scotia coming soon)

• Hybrid Storage: SQLite for structured queries + LanceDB for semantic search

• Natural Language Queries: Ask questions like "What were my dividends in Q3?" or "Show my SPY holdings"

• Structured Tools: Programmatic access via MCP tools

• Archive Management: Keeps original PDFs and parsed JSON for reference

Installation

Configuration

The server is configured via .mcp.json. Key environment variables:

• LANCEDB_PATH: Vector database location (default: ./data/lancedb)

• SQLITE_PATH: SQLite database location (default: ./data/statements.db)

• PDF_ARCHIVE_PATH: Original PDF storage (default: ./data/pdfs)

• JSON_ARCHIVE_PATH: Parsed JSON storage (default: ./data/json)

• MODEL_NAME: Embedding model (default: all-MiniLM-L6-v2)

Usage

Starting the Server

Indexing Statements

Querying Data

Natural Language Search:

Structured Queries:

Resources

• statements://catalog - List all indexed statements

• statements://{statement_id} - Get specific statement

• statements://{statement_id}/summary - Account summary

• statements://{statement_id}/holdings - Holdings/positions

• statements://{statement_id}/transactions - Transaction history

Supported Statements

Questrade

• ✅ Account Summary (balances, FX rates, TFSA info)

• ✅ Holdings/Positions (securities with cost basis, P&L)

• ✅ Transactions (trades, dividends, deposits, withdrawals)

Interactive Brokers (Coming Soon)

• ⏳ Planned

Scotia (Coming Soon)

• ⏳ Planned

Development

Architecture

• FastMCP 2.0: MCP server framework

• pymupdf4llm: PDF parsing optimized for LLMs

• LanceDB: Vector database for semantic search

• SQLite: Structured metadata storage

• sentence-transformers: Embedding generation

License

MIT