| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 18 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Discovers and enumerates tokenized yield vaults across Ethereum and Base networks by querying on-chain vault registries and metadata contracts. Implements network-aware discovery that differentiates between mainnet and Layer 2 deployments, returning vault addresses, underlying asset compositions, and current TVL metrics through MCP tool bindings that abstract RPC calls.
Unique: Provides MCP-native vault enumeration that abstracts away RPC complexity and chain-specific contract ABIs, enabling LLM agents to discover vaults without writing Solidity or Web3.js code. Dual-chain support (Ethereum + Base) in a single tool call.
vs alternatives: Simpler than building custom Web3 indexers or querying The Graph subgraphs; faster than manual vault research because it returns structured, queryable vault metadata in a single MCP call.
Retrieves historical and real-time performance metrics for vaults including APY, yield distribution history, fee structures, and asset allocation changes. Queries on-chain vault state and event logs to compute performance indicators, then normalizes data into standardized metrics accessible via MCP tools. Handles multi-token vault compositions and fee deductions in APY calculations.
Unique: Computes normalized APY metrics across multi-token vaults by parsing on-chain events and state, abstracting away complex fee and yield distribution logic. Integrates fee deductions directly into APY calculations rather than reporting gross yield separately.
vs alternatives: More accurate than static APY displays because it recalculates based on actual on-chain events; more accessible than writing custom subgraph queries because metrics are pre-computed and standardized.
Estimates gas costs for vault operations (deposits, withdrawals, approvals) by simulating transactions and querying current gas prices. Returns gas estimates in wei and USD equivalent using current ETH/USD prices. Supports gas price overrides for custom priority levels.
Unique: Simulates vault operations to estimate gas costs and converts to USD using current ETH prices in a single call. Breaks down gas costs by operation step (approval, deposit, etc.) to help callers understand where costs are incurred.
vs alternatives: More accurate than static gas estimates because it simulates actual transactions; more convenient than manual gas calculations because it handles multi-step operations and USD conversion.
Assesses vault risks including smart contract risk, liquidity risk, concentration risk, and governance risk. Scores vaults on multiple risk dimensions and provides a composite risk score. Analyzes vault TVL, asset concentration, governance structure, and audit status to produce risk metrics.
Unique: Computes multi-dimensional risk scores (smart contract, liquidity, concentration, governance) from on-chain data and produces a composite risk score. Enables risk-aware vault filtering without requiring manual risk analysis.
vs alternatives: More comprehensive than simple TVL-based risk assessment because it evaluates multiple risk dimensions; more accessible than building custom risk models because it returns pre-computed risk scores.
Analyzes historical vault performance over multiple time horizons (1 week, 1 month, 3 months, 1 year, etc.) by computing returns, volatility, and Sharpe ratios. Queries historical share prices and yield distributions to compute performance metrics. Supports comparison of vault performance against benchmarks or other vaults.
Unique: Computes multi-horizon performance metrics (returns, volatility, Sharpe ratio) from historical share prices and yield distributions. Enables performance comparison across vaults without requiring manual calculation.
vs alternatives: More comprehensive than simple APY displays because it shows volatility and risk-adjusted returns; more accessible than building custom performance analysis tools because it returns pre-computed metrics.
Simulates vault rebalancing scenarios to show how asset allocation changes would impact returns and fees. Allows users to specify target allocations and simulates the rebalancing process including transaction costs and slippage. Returns projected outcomes and cost analysis for proposed rebalancing.
Unique: Simulates rebalancing transactions and cost impact in a single call, allowing callers to evaluate rebalancing decisions before execution. Breaks down costs by component (gas, slippage) to help optimize rebalancing strategy.
vs alternatives: More transparent than manual rebalancing because it shows projected costs and outcomes; more efficient than trial-and-error rebalancing because it simulates multiple strategies.
Generates recommendations for optimizing yield by analyzing user's current vault positions and suggesting alternative vaults or rebalancing actions. Uses multi-vault comparison, performance analysis, and fee optimization to identify opportunities for improved risk-adjusted returns. Ranks recommendations by potential impact and implementation cost.
Unique: Generates yield optimization recommendations by analyzing user's current positions and comparing against alternative vaults using multi-dimensional metrics (APY, fees, risk, liquidity). Ranks recommendations by projected impact and implementation cost.
vs alternatives: More personalized than generic vault rankings because it considers user's current positions; more actionable than simple performance comparisons because it provides specific recommendations with projected outcomes.
Aggregates vault positions across Ethereum and Base into a unified view with cross-chain portfolio metrics. Queries vault holdings on both chains, converts to common reference currency, and provides consolidated portfolio analytics. Handles chain-specific gas costs and liquidity considerations in recommendations.
Unique: Aggregates vault positions across Ethereum and Base in a single call, normalizing to common currency and providing unified portfolio metrics. Handles chain-specific considerations (gas costs, liquidity) in recommendations.
vs alternatives: More convenient than querying each chain separately because it returns consolidated metrics; more accurate than manual cross-chain aggregation because it handles currency conversion and chain-specific factors.
+10 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.
atv scores higher at 46/100 vs Supabase at 42/100.
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