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| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes yfinance's ticker data fetching as an MCP tool that LLM agents can invoke to retrieve current and historical stock prices. Implements the Model Context Protocol's tool-calling interface, allowing Claude or other MCP-compatible clients to request stock data through standardized JSON-RPC messages without direct library imports. The server wraps yfinance's Ticker class and marshals responses back through MCP's transport layer (stdio or HTTP).
Unique: Wraps yfinance as a standardized MCP tool, enabling seamless integration into multi-tool agent workflows without requiring agents to manage library imports or API authentication directly. Uses MCP's schema-based tool registration to expose yfinance methods as discoverable, type-safe functions.
vs alternatives: Simpler than building custom REST endpoints for yfinance; integrates directly into Claude and other MCP clients without additional infrastructure, whereas direct yfinance usage requires agents to handle library management and error handling themselves.
Retrieves and structures historical open-high-low-close-volume (OHLCV) candlestick data for specified date ranges and intervals (daily, weekly, monthly). Leverages yfinance's built-in resampling and aggregation to convert raw tick data into standardized time-series format. Returns data as JSON arrays suitable for charting libraries or time-series analysis, with automatic handling of market holidays and trading halts.
Unique: Exposes yfinance's pandas-based resampling as an MCP tool, allowing agents to request pre-aggregated historical data without managing DataFrame transformations themselves. Automatically handles timezone normalization and market calendar adjustments.
vs alternatives: More flexible than static CSV exports because agents can request arbitrary date ranges and intervals on-demand; more accessible than raw yfinance because MCP abstracts pandas/numpy complexity into simple JSON responses.
Fetches corporate action history (dividends, stock splits, rights issues) for a given ticker through yfinance's actions API. Structures this data as MCP tool responses, enabling agents to adjust historical price data or calculate total return metrics that account for distributions. Automatically parses yfinance's internal action tables and normalizes dates and amounts.
Unique: Exposes yfinance's actions API as a discrete MCP tool, allowing agents to separately query corporate actions without fetching full price history. Normalizes dividend and split data into consistent JSON schema for downstream calculations.
vs alternatives: Simpler than manually parsing SEC filings or maintaining a corporate actions database; more reliable than web scraping because it leverages yfinance's curated data, though less comprehensive than premium financial data providers like Bloomberg or FactSet.
Retrieves company metadata (sector, industry, market cap, PE ratio, dividend yield, 52-week range) and fundamental metrics from yfinance's info endpoint. Marshals this data through MCP's tool interface, enabling agents to contextualize price data with company fundamentals. Handles missing fields gracefully and normalizes numeric values across different data types.
Unique: Wraps yfinance's info endpoint as an MCP tool, exposing company fundamentals as a queryable resource for agents without requiring direct library access. Normalizes inconsistent field naming and handles null values across different ticker types.
vs alternatives: More accessible than raw yfinance because MCP abstracts data parsing; less comprehensive than dedicated fundamental data APIs (e.g., Alpha Vantage, IEX Cloud) but requires no additional API keys or subscriptions.
Accepts a list of ticker symbols and returns aggregated price/fundamental data for all tickers in a single MCP call. Implements concurrent fetching using Python's asyncio or threading to parallelize yfinance requests, reducing total latency compared to sequential ticker-by-ticker calls. Structures responses as a JSON array indexed by ticker symbol for easy agent consumption.
Unique: Implements concurrent yfinance fetching within a single MCP tool call, reducing round-trip overhead compared to sequential agent calls. Uses asyncio or threading to parallelize I/O-bound network requests while maintaining MCP's synchronous tool interface.
vs alternatives: Faster than agents making individual MCP calls per ticker because it batches network requests; simpler than agents managing their own concurrency because parallelization is handled server-side.
Retrieves exchange rates and forex data for currency pairs (e.g., EUR/USD) using yfinance's ticker support for forex symbols. Enables agents to convert portfolio values across currencies or analyze forex trends. Handles currency pair naming conventions and returns rates with timestamps for time-series analysis.
Unique: Extends yfinance's ticker support to forex symbols, exposing currency data through the same MCP tool interface as stocks. Normalizes forex pair naming and provides time-series rates for currency analysis.
vs alternatives: More integrated than calling separate forex APIs because it uses the same yfinance backend; less comprehensive than dedicated forex platforms (e.g., OANDA, Interactive Brokers) but requires no additional subscriptions.
Implements robust error handling for yfinance failures (invalid tickers, network timeouts, rate limiting) and returns structured error responses through MCP. Validates input parameters (date ranges, ticker formats) before making requests and provides meaningful error messages to agents. Includes optional fallback strategies such as returning cached data or degraded responses when primary data sources fail.
Unique: Wraps yfinance calls with comprehensive input validation and error handling, returning structured error responses that agents can programmatically interpret. Distinguishes between client errors (invalid input), server errors (yfinance unavailable), and rate limiting to enable targeted recovery strategies.
vs alternatives: More reliable than raw yfinance because it validates inputs and handles failures gracefully; simpler than agents implementing their own error handling because validation and error structuring is centralized in the MCP server.
Implements the Model Context Protocol's transport layer, supporting both stdio (for local Claude Desktop integration) and HTTP (for remote MCP hosts). Abstracts transport details from tool implementations, allowing the same yfinance tools to work across different client architectures. Handles JSON-RPC message serialization, request routing, and response marshaling according to MCP specification.
Unique: Implements both stdio and HTTP transport layers for MCP, allowing the same server to work with Claude Desktop (stdio) or remote clients (HTTP). Uses MCP SDK's transport abstraction to decouple tool logic from protocol details.
vs alternatives: More flexible than stdio-only implementations because it supports both local and remote deployment; simpler than building custom protocol handlers because it leverages the MCP SDK's transport layer.
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs yfinance-mcp-server at 24/100. yfinance-mcp-server leads on ecosystem, while Supabase is stronger on quality.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
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