| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables agents to reason about game states where information is incomplete or hidden from some players, using belief modeling and uncertainty quantification. The agent maintains probabilistic models of opponent states and hidden information, updating beliefs through Bayesian inference as new observations arrive, allowing strategic decision-making under information asymmetry typical in poker, diplomacy, and deception games.
Unique: Focuses specifically on imperfect information game solving through belief-state reasoning rather than perfect information game trees, using probabilistic state tracking to handle hidden information that standard minimax approaches cannot address
vs alternatives: Addresses a gap in standard game-playing agents (which assume perfect information) by explicitly modeling uncertainty and opponent beliefs, enabling competitive play in information-asymmetric games like poker where traditional alpha-beta pruning fails
Constructs and maintains dynamic models of opponent behavior and likely hidden states through Bayesian belief updating and historical action analysis. The system tracks opponent action patterns, infers probability distributions over their possible hands/strategies, and updates these beliefs incrementally as new game information becomes available, enabling adaptive strategy selection based on opponent model predictions.
Unique: Implements incremental Bayesian belief updating specifically for game contexts, allowing real-time refinement of opponent models as new information arrives, rather than batch retraining approaches used in general ML
vs alternatives: More sample-efficient than pure neural network opponent modeling because it leverages game-theoretic structure and explicit probability distributions, enabling faster adaptation with limited game history
Enables agents to plan multi-step strategies that account for deception, bluffing, and information manipulation in competitive multi-agent settings. The planner constructs game trees that model not just opponent actions but opponent beliefs about the agent's state, allowing strategies that exploit information asymmetry through strategic information revelation or concealment. Uses recursive belief modeling to reason about nested levels of strategic thinking.
Unique: Explicitly models recursive belief structures (agent's belief about opponent's belief about agent's state) to enable deception-aware planning, rather than treating deception as a post-hoc strategy overlay
vs alternatives: Outperforms standard minimax in imperfect information games because it reasons about information states and belief manipulation, not just material advantage; enables strategies that pure value-maximization approaches cannot discover
Computes game-theoretic solutions (Nash equilibria, exploitability metrics, best responses) for imperfect information games using algorithms like counterfactual regret minimization (CFR) or similar iterative solution methods. Produces strategy profiles that are provably optimal or near-optimal under game-theoretic assumptions, enabling agents to play unexploitable strategies or measure how exploitable current strategies are.
Unique: Applies counterfactual regret minimization or similar iterative game-solving algorithms to compute provably near-optimal strategies for imperfect information games, grounding agent behavior in game-theoretic guarantees rather than heuristics
vs alternatives: Produces theoretically sound strategies with exploitability bounds, unlike pure RL approaches which may converge to exploitable local optima; enables agents to guarantee performance against worst-case opponents
Reduces the computational complexity of imperfect information games by grouping similar game states into information sets and applying state abstraction techniques. Compresses the game tree by merging states that are strategically equivalent from the agent's perspective, enabling solution computation and planning in games too large for exact analysis. Uses techniques like card clustering, action abstraction, and betting round abstraction.
Unique: Implements domain-specific abstraction techniques (card clustering, betting abstraction) tailored to imperfect information games, rather than generic state compression, enabling more effective dimensionality reduction
vs alternatives: Achieves better solution quality per computational unit than naive state space reduction because it respects game-theoretic structure and information set semantics, ensuring abstracted solutions remain strategically meaningful
Enables agents to make optimal or near-optimal decisions in sequential games where outcomes depend on hidden information and future opponent actions. Integrates belief tracking, value estimation, and action selection to handle the full pipeline of decision-making under uncertainty. Uses techniques like expectimax search, value iteration, or policy gradient methods adapted for imperfect information settings.
Unique: Integrates belief tracking with value estimation in a unified decision pipeline, ensuring that action selection is grounded in current beliefs about hidden states rather than treating belief and value as separate concerns
vs alternatives: More principled than heuristic-based decision rules because it explicitly optimizes expected value under uncertainty; more computationally tractable than full game tree search because it uses value function approximation
Enables agents to learn and adapt strategies through self-play, population-based training, or interaction with other agents in imperfect information games. Implements learning algorithms (e.g., policy gradient, Q-learning variants, or game-theoretic learning) that converge toward improved strategies while handling the non-stationarity of multi-agent learning environments. Tracks learning progress and strategy evolution across training episodes.
Unique: Applies multi-agent RL specifically to imperfect information games where standard single-agent RL assumptions break down, using techniques like belief-based learning or game-theoretic learning rates to handle non-stationarity
vs alternatives: Enables agents to discover strategies through learning rather than hand-coding or game-theoretic computation, allowing discovery of novel tactics and faster adaptation to new opponents compared to static equilibrium strategies
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 Suspicion Agent at 20/100.
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