| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability retrieves a summary of company filings by analyzing SEC EDGAR data to compute filing velocity, material event counts, and disclosure trends. It uses a pre-computation model that synthesizes raw data into structured JSON outputs, allowing for quick access without the need for live querying of the SEC database. The system leverages caching mechanisms to enhance performance and reduce latency.
Unique: Utilizes a pre-computation strategy to synthesize signals from SEC filings rather than raw data retrieval, enhancing speed and usability.
vs alternatives: More efficient than traditional SEC data scraping tools due to its pre-computed summaries, which eliminate the need for real-time data fetching.
This capability extracts and analyzes insider trading activities from SEC Form 3, 4, and 4A filings, generating derived signals that indicate trends in insider behavior. It employs a signal processing algorithm to classify insider activities and provides structured JSON outputs, making it easy for users to interpret the data. The system is designed to highlight significant insider movements that may indicate company performance trends.
Unique: Focuses on deriving actionable insights from insider trading data rather than just reporting raw filing information, enhancing decision-making capabilities.
vs alternatives: Offers more nuanced insights compared to basic insider trading reports by analyzing trends and deriving signals.
This capability identifies and flags institutional investor activities using SEC Form SC 13D and 13F filings. It processes these filings to derive signals related to activist risks and recent institutional movements, providing users with structured JSON outputs. The system is designed to highlight significant changes in institutional holdings that may influence stock performance.
Unique: Integrates real-time analysis of institutional filings to provide immediate insights into market movements, rather than relying on historical data alone.
vs alternatives: More proactive in identifying risks compared to standard institutional reporting tools, which often lack real-time analysis.
This capability analyzes 8-K filings to assess the severity of material events, categorizing them into red, yellow, and green flags based on their potential impact. It uses a severity scoring algorithm that evaluates the content of each filing and generates structured JSON outputs, allowing users to quickly gauge the risk associated with specific events. This approach enables users to prioritize their attention based on the severity of disclosures.
Unique: Employs a severity scoring system that categorizes events into actionable flags, enhancing risk assessment capabilities for users.
vs alternatives: More systematic in flagging material events compared to traditional methods that often rely on manual review.
This capability allows users to compare disclosure signals from multiple companies side-by-side, analyzing derived signals such as filing velocity and material event counts. It utilizes a comparative analysis framework that synthesizes data from various companies into structured JSON outputs, enabling users to identify trends and make informed decisions. The system is designed for quick comparisons to highlight 'winners' in terms of disclosure activity.
Unique: Facilitates detailed comparative analysis of disclosure signals, allowing users to quickly identify trends and make strategic decisions.
vs alternatives: More efficient than manual comparison methods, providing structured insights that are easy to interpret.
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 toolstem-sec-mcp-server 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
+1 more capabilities