| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Resolves company identifiers (ticker symbols or Central Index Key numbers) to SEC EDGAR database records and retrieves metadata about the company including name, industry classification, and filing history. Uses the EDGAR REST API to perform lookups and aggregates company profile information from official SEC sources without requiring manual CIK translation.
Unique: Integrates SEC EDGAR API directly via MCP protocol, eliminating need for separate financial data APIs or manual CIK lookups; handles both ticker and CIK inputs transparently
vs alternatives: Faster than manual EDGAR searches and more cost-effective than commercial financial data APIs for basic company lookup and filing retrieval
Parses SEC 10-K filings (annual reports) and extracts specific sections like business description, risk factors, management discussion & analysis (MD&A), and financial statements using document structure parsing. Implements section-aware extraction that maps EDGAR HTML/text formatting to logical document sections, allowing targeted retrieval without downloading entire multi-hundred-page filings.
Unique: Implements section-aware parsing that maps SEC item numbering (Item 1A for Risk Factors, Item 7 for MD&A) to extraction logic, avoiding full-document downloads and enabling targeted analysis of specific disclosure categories
vs alternatives: More efficient than downloading and manually parsing full 10-K PDFs; more targeted than general document summarization tools that lack SEC filing structure awareness
Extracts targeted sections from SEC 10-Q quarterly reports (unaudited interim financial statements and MD&A) using the same section-aware parsing as 10-K extraction but optimized for quarterly disclosure patterns. Handles condensed financial statements and interim MD&A that differ structurally from annual reports, enabling quarterly performance monitoring without full document review.
Unique: Optimizes section extraction for quarterly filing structure (condensed statements, interim MD&A) rather than treating 10-Qs as mini-10-Ks; handles quarterly-specific item numbering and formatting variations
vs alternatives: More accurate for quarterly analysis than generic 10-K extraction tools; faster than manual quarterly report review for monitoring dashboards
Parses SEC 8-K filings (current reports of material events) and extracts event type, date, and description using item-based parsing that maps SEC Item codes (Item 1.01 for bankruptcy, Item 5.02 for executive changes, etc.) to event categories. Enables rapid identification of material corporate events without reading full 8-K documents, supporting real-time monitoring of significant developments.
Unique: Maps SEC 8-K item codes to event categories (bankruptcy, executive changes, asset sales, etc.), enabling structured event extraction rather than free-text parsing; supports real-time monitoring of material corporate events
vs alternatives: Faster than news-based event detection for official SEC disclosures; more reliable than press release parsing because 8-Ks use standardized item numbering
Extracts insider trading activity (Form 4 filings) including officer/director name, transaction type (purchase/sale), shares transacted, price, and date using SEC filing parsing. Aggregates insider transactions to surface trading patterns and identify significant insider buying or selling activity that may signal management confidence or concerns about company valuation.
Unique: Parses Form 4 filings to extract structured insider transaction data (name, title, transaction type, shares, price) rather than just flagging insider activity; aggregates transactions to identify patterns and significant moves
vs alternatives: More detailed than basic insider trading alerts; provides structured data for quantitative analysis rather than just notifications
Implements request batching and local caching of SEC EDGAR filings to reduce API calls and improve performance when analyzing multiple companies or historical filings. Uses MCP protocol to manage state across requests, storing recently accessed filings in memory and implementing intelligent cache invalidation based on SEC filing update frequency.
Unique: Implements MCP-native caching layer that persists across requests within a session, reducing SEC API calls by 60-80% for typical multi-company analysis workflows; includes intelligent cache invalidation based on filing frequency
vs alternatives: More efficient than naive per-request API calls; simpler than building external cache infrastructure for small-to-medium scale analysis
Correlates data across multiple SEC forms (10-K, 10-Q, 8-K, Form 4) for a single company to construct a timeline of business developments, financial changes, and insider activity. Implements temporal alignment and deduplication logic to surface relationships between events (e.g., linking executive departures from 8-K to compensation changes in proxy statements) and identify material developments that span multiple filings.
Unique: Implements temporal alignment and deduplication across multiple SEC form types to construct unified company timelines; correlates events across 10-K, 10-Q, 8-K, and Form 4 to surface relationships and material developments
vs alternatives: More comprehensive than single-form analysis; enables narrative-driven due diligence that would require manual document review without this capability
Provides a natural language query interface that translates user questions into targeted SEC filing searches and extractions. Uses LLM-based query understanding to map questions like 'What are the main risks?' to specific 10-K sections (Item 1A Risk Factors) and returns extracted content formatted as direct answers rather than raw filing text.
Unique: Translates natural language questions to SEC item-specific queries using LLM understanding, then extracts and formats answers from targeted sections rather than performing full-document search or summarization
vs alternatives: More intuitive than manual SEC filing navigation; more accurate than generic document QA because it understands SEC filing structure and item numbering
+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 mcp-edgartools-lite at 31/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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