Supabase ranks higher at 42/100 vs AgentFi — DeFi Tools for AI Agents at 35/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | AgentFi — DeFi Tools for AI Agents | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 35/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality |
| 0 |
| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Executes token swaps across EVM chains (Ethereum, Base, Arbitrum, Polygon) by constructing swap transactions through DEX aggregator integration, simulating execution on-chain via eth_call to validate slippage and gas costs before broadcasting to the network. Uses Safe smart wallet as execution context with MPC key management via Turnkey, ensuring swaps are policy-constrained and reversible until final broadcast.
Unique: Combines pre-broadcast eth_call simulation with Safe smart wallet + Turnkey MPC key management, allowing agents to validate DeFi transactions before irreversible execution while maintaining non-custodial security model. Most competitors either skip simulation (fast but risky) or use centralized key storage (simpler but less secure).
vs alternatives: Provides simulation-before-broadcast safety that centralized wallet services lack, while maintaining non-custodial security that pure simulation libraries don't offer.
Queries real-time token balances and market prices across EVM chains by calling ERC-20 balanceOf() for on-chain state and integrating with price oracles (Chainlink, Uniswap TWAP, or DEX spot prices) to return current portfolio value. Caches price data with configurable TTL to reduce oracle calls and RPC load, returning structured balance/price tuples for agent decision-making.
Unique: Integrates balance queries with multi-source price oracles (Chainlink, Uniswap TWAP, DEX spot) and implements configurable caching to reduce RPC load while maintaining freshness. Most agent frameworks either hardcode a single price source or require manual oracle integration.
vs alternatives: Faster than querying Chainlink directly for every balance check due to local caching, while more accurate than DEX spot prices alone by supporting TWAP and multiple oracle sources.
Simulates pending transactions using eth_call RPC method to execute transactions in a read-only mode, returning the expected output (swap amounts, collateral ratios, transfer status, etc.) without modifying blockchain state. Detects transaction failures (reverts, out-of-gas, insufficient balance) before broadcast, and provides detailed error messages to help agents understand why transactions would fail. Integrates with all DeFi operations to provide pre-broadcast validation.
Unique: Integrates eth_call simulation into the MCP tool layer before transaction construction, allowing agents to validate transactions without broadcasting — most agent tools either skip simulation or require agents to implement simulation logic themselves
vs alternatives: Reduces failed transaction costs vs. broadcast-first approaches, and provides detailed error messages vs. generic RPC errors
Supplies ERC-20 tokens to Aave lending pools and manages collateral by calling Aave V3 contract functions (supply, withdraw, borrow, repay) through Safe smart wallet. Validates collateral ratios and liquidation thresholds before execution via simulation, preventing over-leveraged positions. Tracks aToken balances and interest accrual to enable yield farming strategies.
Unique: Wraps Aave V3 contract calls with pre-execution collateral ratio validation and liquidation threshold checks via eth_call simulation, preventing agents from entering over-leveraged states. Integrates with Safe smart wallet for non-custodial execution, unlike centralized lending APIs.
vs alternatives: Safer than direct Aave contract calls because it validates collateral ratios before execution, while more flexible than Aave's own risk management because agents can define custom thresholds.
Enforces agent-specific transaction policies (spending limits, token whitelists, counterparty restrictions) by validating each transaction against a policy rule engine before Safe smart wallet execution. Policies are defined as JSON schemas and evaluated server-side; transactions violating policies are rejected before broadcast. Supports approval workflows where high-value transactions require human or oracle confirmation.
Unique: Implements server-side policy rule engine that validates transactions against agent-specific schemas before Safe wallet execution, enabling fine-grained spending controls and approval workflows. Most agent frameworks lack built-in policy enforcement; developers must implement custom guards.
vs alternatives: More flexible than fixed spending limits because policies can encode complex rules (token whitelists, counterparty restrictions), while faster than human-in-the-loop approval for low-risk transactions due to automatic approval for policy-compliant actions.
Provisions a dedicated Safe smart wallet for each agent by deploying a Safe proxy contract and registering MPC-managed keys via Turnkey's key management service. Turnkey handles key generation, signing, and rotation without exposing private keys to the application layer. Each agent receives a unique wallet address and can execute transactions through Safe's multi-sig interface with MPC-backed signatures.
Unique: Integrates Safe smart wallets with Turnkey's MPC key management, eliminating private key exposure while maintaining non-custodial control. Keys are generated and stored in Turnkey's secure enclave; agent never handles raw key material. Most agent frameworks either use EOA wallets (key exposure risk) or centralized custody (non-custodial risk).
vs alternatives: More secure than EOA wallets because keys are MPC-managed and never exposed, while more autonomous than multi-sig wallets requiring manual approvals because Turnkey handles signing automatically.
Transfers ERC-20 tokens or native chain assets (ETH, MATIC, ARB) from agent wallet to recipient addresses via Safe smart wallet, with optional recipient validation (checking if address is a contract, known exchange, or whitelisted address) and memo/note attachment for transaction tracking. Validates recipient address format and checks for common mistakes (sending to token contract address, zero address).
Unique: Wraps ERC-20 and native asset transfers with recipient validation heuristics (contract detection, exchange address checking) and optional memo attachment, reducing common transfer mistakes. Most agent frameworks provide raw transfer functions without validation.
vs alternatives: Safer than raw transfer calls because it validates recipient addresses before execution, while more flexible than whitelisted-only transfers because it supports dynamic recipient validation.
Exposes DeFi capabilities as MCP (Model Context Protocol) tools with JSON schema definitions, allowing LLM agents to discover and invoke DeFi functions through standard MCP function-calling interface. Each tool includes input/output schemas, descriptions, and error handling. Agent can call tools via MCP client library, which handles schema validation, parameter marshalling, and response parsing.
Unique: Implements MCP tool schema registration for all DeFi operations, enabling LLM agents to discover and call functions through standard MCP interface rather than hardcoded function names. Schemas include input/output validation and error handling, reducing agent hallucination about function signatures.
vs alternatives: More flexible than hardcoded function bindings because schemas enable dynamic tool discovery, while more reliable than natural language function descriptions because schemas enforce strict parameter validation.
+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.
Supabase scores higher at 42/100 vs AgentFi — DeFi Tools for AI Agents at 35/100.
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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