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| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows users to execute strategy backtests remotely by specifying a symbol, timeframe, and date range. It utilizes a Model Context Protocol (MCP) to communicate with the backtest engine, ensuring that the requests and responses are structured and consistent. The architecture is designed to handle multiple backtest requests while enforcing rate limits, making it efficient for users who need to test various strategies in a timely manner.
Unique: The use of a remote MCP endpoint allows for seamless integration with various trading strategies without requiring local execution, providing flexibility and scalability.
vs alternatives: More efficient than local backtesting tools as it allows for concurrent execution of multiple strategies without local resource constraints.
This capability enables users to programmatically pass strategy inputs to the backtest engine, allowing for dynamic adjustments and testing of different parameters. It leverages structured data formats to ensure that inputs are correctly interpreted by the engine, facilitating a smooth integration with automated trading systems. This design choice enhances the flexibility and usability of the backtesting process.
Unique: Supports dynamic parameter passing through structured data, allowing for real-time adjustments during backtesting, which is not commonly found in traditional backtesting tools.
vs alternatives: More versatile than static input methods, enabling rapid iteration and testing of strategies without manual intervention.
This capability provides users with structured backtest results, including detailed metrics such as trades, win rate, profit, and drawdown. The results are formatted in a way that allows for easy interpretation and further analysis, supporting decision-making processes. The architecture ensures that results are consistently formatted and can be easily integrated into reporting tools or dashboards.
Unique: Delivers results in a structured format that is consistent across different backtests, making it easier to compare and analyze performance metrics.
vs alternatives: More comprehensive than basic logging tools, providing detailed performance insights that are ready for analysis.
This capability keeps users informed about the progress of long-running backtests through real-time notifications. It employs a notification system that updates users on the status of their backtests, allowing them to monitor performance without needing to manually check the results. This feature is particularly useful for extensive backtesting scenarios where wait times can be significant.
Unique: Utilizes a dedicated notification system that provides real-time updates on backtest progress, enhancing user experience during lengthy operations.
vs alternatives: More user-friendly than traditional backtesting interfaces that require manual refreshes to check status.
This capability allows users to request the agent to write a trading strategy based on specified criteria. It uses natural language processing to interpret user requests and generate PineScript code that aligns with the user's trading objectives. This feature streamlines the strategy development process, making it accessible even to those with limited coding experience.
Unique: Incorporates NLP to understand user requests and generate relevant PineScript code, making strategy development more intuitive for non-coders.
vs alternatives: More accessible than traditional coding environments, enabling users to create strategies without deep programming knowledge.
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 AIAgentwithPineScript at 30/100.
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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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