| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Automatically crawls and ingests real-time data from Twitter/X, Reddit, StockTwits, and financial forums using API integrations and web scraping pipelines. The system maintains persistent connections to high-velocity data sources and normalizes heterogeneous post formats into a unified internal representation, enabling downstream NLP analysis on a consolidated dataset rather than requiring manual source-by-source monitoring.
Unique: Purpose-built for retail stock market chatter rather than generic social media monitoring; prioritizes financial forums and trading communities over general social networks, with ticker symbol extraction and financial context awareness baked into the pipeline
vs alternatives: Faster than manual Reddit/Twitter scrolling and more focused than generic social listening tools like Brandwatch, but slower and less comprehensive than institutional Bloomberg terminals with proprietary data feeds
Applies fine-tuned NLP models (likely transformer-based, possibly BERT or GPT variants) to classify social posts as bullish, bearish, or neutral sentiment, then aggregates sentiment scores at the ticker level to identify emerging trends. The system likely uses attention mechanisms to weight recent posts more heavily and detect sentiment shifts, distinguishing genuine catalysts from noise through pattern matching against historical trend data.
Unique: Specialized financial sentiment models trained on market-specific language and retail investor vernacular rather than generic social media sentiment classifiers; likely includes domain-specific lexicons for financial terms and trading slang
vs alternatives: More accurate for stock-specific sentiment than general-purpose sentiment APIs like AWS Comprehend, but less sophisticated than institutional sentiment platforms like Refinitiv or MarketPsych which use proprietary training data and expert labeling
Provides educational content, tooltips, and contextual guidance to help retail investors understand how to interpret social signals and avoid common pitfalls (false positives, pump-and-dumps, sentiment lag). The system likely includes explainability features showing which posts or keywords drove a sentiment classification, helping users build intuition about signal quality.
Unique: Focuses on teaching retail investors how to interpret social signals rather than just providing raw data; includes explainability features to build user trust
vs alternatives: More educational than data-only platforms, but less comprehensive than dedicated trading education platforms or financial advisors
Monitors velocity and acceleration of mention counts, sentiment shifts, and engagement metrics across aggregated posts to identify stocks entering a trend phase. Uses statistical anomaly detection (likely z-score, isolation forest, or LSTM-based approaches) to flag when a ticker's social activity deviates significantly from its baseline, then ranks emerging trends by strength, velocity, and consistency to surface the most actionable signals.
Unique: Combines mention velocity, sentiment acceleration, and engagement metrics into a composite trend score rather than relying on single-signal detection; likely uses market-regime-aware baselines that adjust for bull/bear/sideways conditions
vs alternatives: More responsive than traditional technical analysis indicators which lag price by definition, but less predictive than institutional order flow analysis or options market positioning data
Uses NLP entity extraction and event detection models to identify specific catalysts mentioned in social posts (earnings dates, FDA approvals, product launches, insider trading, litigation, etc.) and correlates them with sentiment and volume spikes. The system likely maintains a knowledge base of known catalyst types and uses pattern matching to extract structured event metadata from unstructured text, then surfaces these events with context to help investors understand the 'why' behind sentiment shifts.
Unique: Focuses on extracting actionable catalysts from retail chatter rather than just aggregating sentiment; likely uses financial domain-specific NER models and event type taxonomies tailored to stock market catalysts
vs alternatives: Faster than manual news reading and catches early social signals before mainstream media, but less reliable than official company disclosures or SEC filings which institutional investors use
Allows users to create custom watchlists of tickers and configure alert thresholds for sentiment changes, trend emergence, mention velocity, and specific catalysts. The system stores user preferences and maintains state to deliver notifications (email, push, in-app) when conditions are met, likely using a rule engine to evaluate conditions against real-time data streams and debounce alerts to avoid notification fatigue.
Unique: Tailored for retail investors with simple threshold-based rules rather than complex ML-driven personalization; focuses on ease of configuration over sophistication
vs alternatives: More accessible than institutional alert systems like Bloomberg terminals which require complex configuration, but less sophisticated than ML-driven recommendation engines that learn from user behavior
Maintains a time-series database of historical sentiment, mention volume, and trend scores for each ticker, allowing users to query past trends and correlate them with price movements. The system likely provides visualization tools (charts, heatmaps) to show how social sentiment preceded or lagged price action, and may include basic backtesting functionality to measure the predictive power of social signals over historical periods.
Unique: Provides historical social signal data that retail investors typically lack access to; most retail platforms focus on real-time data only, not historical trend archives
vs alternatives: More accessible than institutional research platforms with historical sentiment archives, but less comprehensive than academic datasets or proprietary hedge fund data
Analyzes social sentiment and mention patterns across related stocks (same sector, competitors, supply chain) to identify sector-wide trends and identify which stocks are leading vs. lagging sentiment shifts. The system likely uses clustering algorithms to group related stocks and compares their sentiment trajectories to surface relative strength and identify potential rotation opportunities.
Unique: Extends sentiment analysis beyond individual stocks to sector-level patterns, helping investors understand whether a move is idiosyncratic or part of broader trend
vs alternatives: More granular than sector ETF tracking but less sophisticated than institutional sector rotation models that incorporate macro data and options positioning
+3 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.
Uptrends.ai scores higher at 44/100 vs Supabase at 42/100. Uptrends.ai leads on adoption and quality, while Supabase is stronger on ecosystem. However, Supabase offers a free tier which may be better for getting started.
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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