Added many improvements to the core library but also to the performance of the related MCP server.
Core Additions
- Included a year more data on each start_date request: this makes it possible to e.g. if you are looking for 2015-2026 data, metrics that are calculated based on the average of two balance sheet statement items (such as Return on Equity) don't have an empty column to start with. Same goes for when calculating growth metrics.
- Fiscal year → calendar year normalisation: Companies with non-calendar fiscal years are now remapped to the corresponding calendar year, enabling consistent cross-company comparisons when mixing tickers (e.g. a company with a March fiscal year end is assigned to the correct calendar year). This is common practice to allow for company comparisons.
- Added show_columns filtering: allows to be more selective with your calls e.g. by adding
show_columns=['Revenue']to the income statement call gets you only revenues. - Replaced
tqdmwith logger statements: Progress bars removed across all data-fetching modules (fmp_model,yfinance_model,historical_model,fundamentals_model,currencies_model,portfolio_model,options). Replaced with structuredlogger.infocalls.tqdmremoved as a dependency.
MCP Server
- Changed response from markdown tables to JSON: this strongly improves the speed of the LLM's response given that it can parse these type of tables better.
- Transformation notes in responses: MCP responses now include a
_noteskey when data transformations were applied, surfacing fiscal year remapping and currency conversions transparently to the LLM. - Column filtering (
show_columns): All MCP tools now support ashow_columnsparameter to narrow the returned columns post-computation, reducing response size and token usage. - Removed rounding from MCP layer: Rounding is no longer applied server-side; the LLM is responsible for formatting.
- Removed SKILL.md routing: Simplified MCP server startup, tool dispatch no longer depends on a SKILL.md file, reducing startup complexity.