| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Fetches current stock prices, bid-ask spreads, and market data from Yahoo Finance API through the Model Context Protocol, enabling LLM agents to query live market data without direct API integration. Implements MCP tool schema binding to expose Yahoo Finance endpoints as callable functions that Claude or other MCP-compatible models can invoke with ticker symbols as parameters.
Unique: Exposes Yahoo Finance as an MCP tool, allowing LLM agents to call financial data retrieval as a native function without requiring the model to handle HTTP requests or API authentication directly. Uses MCP's standardized tool schema to abstract away API complexity.
vs alternatives: Simpler integration than building custom REST API wrappers for each LLM framework — MCP standardization means one server works with Claude, any MCP-compatible model, and multiple agent frameworks without code duplication.
Queries Yahoo Finance historical price data (OHLCV: open, high, low, close, volume) for a given ticker and date range, returning time-series data suitable for technical analysis or backtesting. Implements date parameter validation and pagination to handle large datasets, exposing the data through MCP tool interface for agent-driven historical analysis workflows.
Unique: Integrates historical data retrieval as an MCP tool, allowing agents to autonomously fetch and analyze multi-year price histories without requiring manual data downloads or external data pipeline setup. Abstracts pagination and date validation logic within the MCP server.
vs alternatives: Faster agent iteration than manual CSV imports or direct API calls — agents can request historical data inline during reasoning, enabling dynamic analysis without context switching to external tools.
Retrieves company financial metrics (P/E ratio, market cap, dividend yield, earnings per share, debt-to-equity, etc.) from Yahoo Finance for fundamental analysis. Implements MCP tool binding to expose these metrics as queryable data, enabling agents to perform valuation comparisons and fundamental screening without manual data aggregation.
Unique: Exposes Yahoo Finance fundamentals as MCP tools, allowing agents to query financial metrics inline during analysis without requiring separate data sources or manual metric aggregation. Standardizes metric naming and format across the MCP interface.
vs alternatives: More accessible than building custom SEC filing parsers or maintaining multiple financial data subscriptions — agents get standardized fundamental data through a single MCP interface.
Enables agents to request data for multiple tickers in a single MCP tool invocation or through sequential calls with optimized batching. Implements parameter arrays or loop-friendly tool schemas to allow agents to analyze portfolios or compare multiple securities without individual API calls per ticker. Handles response aggregation and formatting for comparative analysis.
Unique: Implements batch-friendly MCP tool schemas that allow agents to request data for multiple tickers efficiently, reducing round-trip overhead compared to single-ticker tools. Aggregates responses in a format optimized for comparative analysis.
vs alternatives: Reduces agent latency for portfolio analysis compared to sequential single-ticker calls — agents can analyze 10-stock portfolios in one tool invocation instead of 10 separate API calls.
Implements the Model Context Protocol server specification, handling client connection negotiation, tool schema registration, and request routing. Manages the server process lifecycle (startup, shutdown, error handling) and exposes Yahoo Finance capabilities as standardized MCP tools with JSON schema definitions. Handles protocol versioning and capability advertisement to ensure compatibility with MCP clients.
Unique: Implements full MCP server lifecycle management, handling protocol negotiation, tool schema registration, and request routing according to the MCP specification. Abstracts away JSON-RPC complexity from tool developers.
vs alternatives: Standardized MCP implementation means the server works with any MCP-compatible client without custom adapters — Claude Desktop, custom runners, and future MCP tools all connect to the same server without modification.
Implements error handling for Yahoo Finance API failures (rate limits, timeouts, invalid tickers, network errors) with appropriate MCP error responses. Provides fallback strategies such as cached data, partial results, or clear error messages that agents can interpret and act upon. Distinguishes between transient errors (retry-able) and permanent failures (invalid ticker).
Unique: Implements MCP-compliant error responses that distinguish between transient API failures and permanent errors, allowing agents to make intelligent retry decisions. Provides structured error information that agents can parse and act upon.
vs alternatives: Better agent resilience than naive API wrappers that crash on errors — agents receive clear error codes and can implement retry logic or fallback strategies without manual error parsing.
Fetches dividend history, upcoming dividend dates, and earnings information from Yahoo Finance for a given ticker. Returns structured data including dividend per share, ex-dividend dates, earnings dates, and EPS estimates. Enables fundamental analysis and income-focused portfolio evaluation through Claude's reasoning capabilities.
Unique: Exposes dividend and earnings data as MCP tools, enabling Claude to incorporate fundamental factors into investment analysis without requiring separate financial data sources. Structures data to support both historical analysis and forward-looking decision-making.
vs alternatives: More comprehensive than price-only analysis because it includes income and earnings factors; more accessible than building custom fundamental analysis tools because data is pre-fetched and structured.
Retrieves information about stock splits, reverse splits, mergers, and other corporate actions from Yahoo Finance. Enables accurate historical price analysis by accounting for adjustments and helps Claude understand why historical prices may appear discontinuous. Integrates with historical price retrieval to provide context for price anomalies.
Unique: Provides corporate action history as a dedicated MCP tool, enabling Claude to automatically detect and explain price discontinuities in historical data. Integrates with historical price retrieval to provide context for analysis.
vs alternatives: More transparent than pre-adjusted historical prices because it shows the actual corporate actions; more useful than ignoring splits because Claude can explain historical anomalies to users.
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 yahoo-finance-mcp at 25/100. yahoo-finance-mcp 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
+1 more capabilities