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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Accepts free-form natural language questions about stocks, market trends, and financial metrics, then routes them through an LLM-based query parser that translates user intent into structured data requests. The system interprets colloquial financial terminology (e.g., 'Is Apple overvalued?', 'What's the tech sector doing?') and maps these to underlying market data APIs, returning conversational responses rather than raw database results.
Unique: Uses LLM-based intent parsing to translate colloquial financial questions directly into market data API calls, eliminating the need for users to learn ticker symbols, financial metrics terminology, or database query syntax. Most competitors require structured input (ticker + metric selection) or charge for natural language access.
vs alternatives: More accessible than Bloomberg Terminal or FactSet for casual users because it removes the learning curve of financial databases, but less reliable than professional tools because LLM parsing can hallucinate or misinterpret financial intent.
Integrates with multiple real-time market data providers (likely Yahoo Finance, Alpha Vantage, or similar free/freemium APIs) to fetch current stock prices, volume, intraday movements, and sector performance. Implements a caching layer to reduce API call frequency and costs, with TTL-based invalidation to balance freshness against rate limits. The system normalizes data from heterogeneous sources into a unified schema before serving to the LLM context.
Unique: Abstracts away the complexity of integrating multiple free market data APIs by normalizing heterogeneous schemas and implementing intelligent caching with TTL-based invalidation. Most competitors either lock data behind paywalls or require users to manage API integrations themselves.
vs alternatives: Cheaper than professional data terminals (Bloomberg, FactSet) because it leverages free APIs, but less reliable and slower because free providers have rate limits and delayed updates compared to institutional-grade feeds.
Takes aggregated market data and user queries, then uses an LLM (likely GPT-3.5 or similar) to generate contextual financial analysis, trend interpretation, and investment thesis summaries. The system constructs prompts that inject current market data, historical context, and financial metrics into the LLM's context window, then post-processes outputs to extract key insights. No human financial analyst reviews outputs before serving to users.
Unique: Combines real-time market data injection with LLM-based analysis to generate contextual financial narratives without human analyst review. Unlike professional research firms, it prioritizes speed and accessibility over accuracy and accountability, making it fundamentally a supplementary tool rather than a primary research layer.
vs alternatives: Faster and cheaper than hiring a financial analyst or subscribing to research platforms, but unreliable for critical investment decisions because LLMs hallucinate financial facts and lack accountability standards of licensed advisors.
Enables users to query multiple stocks simultaneously and receive comparative metrics (valuation ratios, growth rates, sector positioning, relative performance). The system batches ticker lookups to minimize API calls, aggregates results into a unified comparison table, and uses the LLM to generate narrative comparisons (e.g., 'Stock A is cheaper than Stock B on a P/E basis but has slower growth'). Supports sector-level aggregation to identify relative strength across industries.
Unique: Automates multi-stock comparison by batching API calls and using LLM-generated narratives to explain relative positioning, eliminating manual spreadsheet work. Most free tools require users to manually pull data for each stock; professional tools charge for this capability.
vs alternatives: More accessible than FactSet or Bloomberg for casual comparison, but less reliable because LLM-generated comparisons can miss accounting nuances and statistical significance that professional analysts would catch.
Maintains conversation history within a user session, allowing follow-up questions that reference previous queries without re-stating context (e.g., 'How does that compare to its 52-week high?' after asking about current price). The system stores recent queries and responses in session state, injects relevant context into subsequent LLM prompts, and manages context window size to avoid exceeding token limits. No persistent storage across sessions; history is cleared when user closes the browser.
Unique: Implements lightweight session-based context management that allows multi-turn financial conversations without requiring users to repeat context, while avoiding the complexity and cost of persistent storage. Most free financial tools are single-query interfaces; professional platforms charge for conversation history.
vs alternatives: More conversational than traditional financial databases or search engines, but less persistent than professional research platforms because session memory is ephemeral and not cross-device.
Aggregates market data across multiple stocks within a sector to compute sector-level metrics (average P/E, median growth rate, sector momentum, relative strength vs. S&P 500). Uses LLM to interpret these aggregates and identify sector rotation patterns, leadership changes, and macroeconomic drivers. Supports hierarchical sector classification (e.g., Technology > Software > SaaS) to enable drill-down analysis.
Unique: Automates sector-level analysis by aggregating constituent stock data and using LLM to interpret macro trends, eliminating manual spreadsheet work. Most free tools focus on individual stocks; sector analysis is typically locked behind professional platforms.
vs alternatives: More accessible than professional sector research tools, but less reliable because aggregation logic is opaque and LLM narratives can overfit to recent price movements rather than fundamental drivers.
Extracts key financial metrics (P/E ratio, dividend yield, debt-to-equity, ROE, free cash flow, earnings growth) from market data APIs and normalizes them into a consistent schema. Handles missing data gracefully (e.g., dividend yield is N/A for non-dividend stocks) and computes derived metrics (e.g., PEG ratio from P/E and growth rate). Provides both raw metrics and LLM-generated interpretations (e.g., 'P/E of 15 is below historical average, suggesting undervaluation').
Unique: Normalizes heterogeneous fundamental data from free APIs into a consistent schema and provides LLM-generated interpretations, making financial metrics accessible to non-technical users. Most free tools either show raw metrics without context or charge for interpreted analysis.
vs alternatives: More accessible than financial databases for casual users because it explains metrics in plain English, but less reliable than professional research because metrics are stale and lack accounting adjustments.
Allows users to create watchlists of stocks and set price-based alerts (e.g., 'notify me if Apple drops below $150'). Stores watchlist state in browser session or optional user account, periodically polls market data APIs to check alert conditions, and triggers notifications when thresholds are breached. Supports multiple alert types (price level, percentage change, volume spike) and notification channels (in-app, email if account is linked).
Unique: Provides lightweight watchlist and alert management without requiring paid subscriptions or complex setup, leveraging free market data APIs and browser-based state management. Most free tools lack alert functionality; professional platforms charge for this feature.
vs alternatives: More accessible than paid alert services because it's free and requires no setup, but less reliable because polling frequency is limited by API rate limits and alerts may trigger with significant delays.
+1 more capabilities
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 StockGPT at 40/100. StockGPT leads on adoption and quality, while Supabase is stronger on ecosystem.
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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