yfinance-mcp-server
MCP ServerFreeMCP server: yfinance-mcp-server
Capabilities7 decomposed
real-time stock price retrieval via mcp protocol
Medium confidenceExposes yfinance's stock ticker data fetching through MCP server tools, allowing Claude and other MCP clients to query current and historical stock prices by ticker symbol. Implements MCP tool schema binding that translates natural language requests into yfinance API calls, handling ticker validation and price data serialization back to the client as structured JSON responses.
Bridges yfinance (Python financial data library) directly into MCP protocol as callable tools, eliminating the need for custom REST API wrappers or direct library imports in client code. Uses MCP's tool schema system to expose yfinance methods as first-class client capabilities.
Simpler than building a custom REST API wrapper around yfinance; tighter integration with Claude and MCP ecosystem than calling yfinance directly from Python scripts
historical stock data aggregation and time-series export
Medium confidenceRetrieves multi-period historical OHLCV (open, high, low, close, volume) data for a given ticker and date range, aggregating yfinance responses into structured time-series format. Handles date range validation, period granularity selection (daily, weekly, monthly), and formats output as JSON arrays or CSV-compatible structures suitable for analysis or downstream processing.
Exposes yfinance's period-based data fetching (daily, weekly, monthly) as MCP tools with automatic date range validation and format conversion, allowing clients to request historical data without managing yfinance's pandas DataFrame output directly.
More flexible than static data exports; allows dynamic date range queries within MCP conversations vs. pre-computed CSV files
company fundamentals and metadata retrieval
Medium confidenceFetches company-level metadata and fundamental metrics (market cap, P/E ratio, dividend yield, sector, industry, 52-week high/low) from yfinance's Ticker object, exposing these as MCP tools. Implements lazy-loading of ticker info to minimize network requests, caching metadata within a single MCP session, and serializing complex objects (e.g., company info dictionaries) into JSON-safe formats.
Wraps yfinance's Ticker.info dictionary (which returns inconsistent, nested JSON) into a normalized MCP tool schema with optional field filtering, allowing clients to request specific fundamentals without handling yfinance's raw data structure.
Simpler than parsing yfinance's raw info dict in client code; more complete than REST APIs that only expose price data
dividend and earnings history lookup
Medium confidenceRetrieves historical dividend payments and earnings dates for a ticker using yfinance's dividends and earnings attributes, formatting them as time-indexed JSON arrays. Handles missing data gracefully (some tickers have no dividend history), validates date ranges, and provides both raw dividend amounts and calculated metrics like dividend yield and payout frequency.
Exposes yfinance's dividends and earnings Series objects as queryable MCP tools with automatic date filtering and yield calculation, avoiding the need for clients to manipulate pandas Series directly.
More accessible than raw yfinance API for dividend queries; integrated into MCP workflow vs. separate dividend data source
stock split and corporate action history
Medium confidenceRetrieves historical stock splits and corporate actions (reverse splits, mergers, spinoffs) from yfinance's splits attribute, providing adjusted share counts and split ratios. Implements date-indexed lookup allowing clients to understand historical share count changes and their impact on price comparisons across split events.
Surfaces yfinance's splits Series as a queryable MCP tool with automatic ratio calculation and date indexing, enabling agents to understand and adjust for historical corporate actions without manual data wrangling.
More transparent than pre-adjusted price data; integrated into MCP workflow vs. requiring external corporate action databases
multi-ticker batch data retrieval with mcp tool composition
Medium confidenceEnables MCP clients to request data for multiple tickers in a single logical operation by composing individual ticker tools into batch queries, handling parallel or sequential fetching depending on MCP client implementation. Implements error handling per ticker (one failure doesn't block others) and aggregates results into a unified response structure suitable for comparative analysis.
Leverages MCP's tool-calling protocol to enable batch queries without implementing a custom batch endpoint; clients compose multiple ticker tools into a single logical batch operation, with error handling per ticker.
More flexible than a single batch endpoint; allows clients to mix and match tools (price, fundamentals, dividends) per ticker without predefined batch schemas
mcp protocol compliance and tool schema generation
Medium confidenceImplements the Model Context Protocol (MCP) server specification, automatically generating tool schemas for all yfinance capabilities and exposing them via MCP's tool-calling interface. Handles MCP request/response serialization, tool discovery, and parameter validation according to MCP spec, allowing any MCP-compatible client (Claude, custom agents) to discover and invoke yfinance tools without prior knowledge of their signatures.
Implements full MCP server specification with automatic tool schema generation from yfinance methods, enabling zero-configuration integration with MCP clients; uses MCP's standardized tool discovery and invocation protocol rather than custom REST or gRPC APIs.
More standardized than custom REST wrappers; tighter integration with Claude and MCP ecosystem than direct yfinance imports
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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yfinance-mcp-server
MCP server: yfinance-mcp-server
Octagon
** - Deliver real-time investment research with extensive private and public market data.
Signal Synthesis
Access real-time market data and historical financial records from multiple financial data providers. Synthesize market signals to gain deeper insights into stock performance and trends. Streamline financial research with unified access to quotes, intraday bars, and symbol searches.
Best For
- ✓AI developers building financial analysis agents with Claude
- ✓Teams integrating stock market data into MCP-based workflows
- ✓Solo developers prototyping investment research tools
- ✓Quantitative analysts building backtesting systems
- ✓Financial researchers analyzing historical price patterns
- ✓MCP agents performing multi-step financial analysis workflows
- ✓Investment research agents analyzing company fundamentals
- ✓Portfolio analysis tools comparing metrics across multiple stocks
Known Limitations
- ⚠yfinance relies on web scraping Yahoo Finance, which can be rate-limited or break if Yahoo changes HTML structure
- ⚠No built-in caching — repeated queries for the same ticker incur network latency
- ⚠Historical data depth depends on yfinance's data source availability, typically 20+ years but not guaranteed for all tickers
- ⚠Real-time data is delayed by 15-20 minutes for free tier (Yahoo Finance limitation, not MCP server limitation)
- ⚠yfinance data quality varies by ticker and historical period; some delisted or penny stocks have incomplete data
- ⚠Large date ranges (10+ years) may timeout or return incomplete data depending on yfinance source stability
Requirements
Input / Output
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