| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Computes numerical risk scores for transactions, decisions, or business events by applying configurable scoring models through MCP tool endpoints. The system accepts transaction context (amount, user profile, historical patterns, geographic data) and returns normalized risk scores (typically 0-100 or 0-1 scale) that indicate likelihood of fraud, default, or policy violation. Scoring logic is abstracted behind MCP tool interfaces, allowing pluggable risk models (rule-based, ML-based, or hybrid) without client-side implementation.
Unique: Exposes risk evaluation as standardized MCP tool endpoints, enabling any MCP-compatible client (Claude, custom agents, workflow engines) to invoke risk models without SDK dependencies or direct model access. Decouples risk model deployment from client application logic.
vs alternatives: Unlike point-solution fraud APIs (Stripe Radar, Kount), ActionGate's MCP abstraction allows teams to plug in proprietary or open-source risk models and integrate scoring into broader agent-driven workflows without vendor lock-in.
Simulates the predicted consequences of approving, rejecting, or conditionally gating a transaction or decision by running forward-looking models that estimate downstream effects (revenue impact, fraud loss, customer churn, compliance risk). The simulation engine accepts a proposed action and current context, then returns projected outcomes across multiple dimensions (financial, operational, regulatory). This enables decision-makers to evaluate trade-offs before committing to a policy.
Unique: Integrates outcome simulation as a first-class MCP tool, allowing agents to reason about decision consequences within a single conversation context. Simulation results feed directly into downstream decision logic without round-tripping to external systems.
vs alternatives: Compared to static decision rules or lookup tables, ActionGate's simulation capability enables dynamic, context-aware decision-making that accounts for trade-offs. Unlike academic simulation frameworks (AnyLogic, SimPy), ActionGate is purpose-built for real-time business decision support and integrates natively with agent workflows.
Enforces business policies by evaluating transactions against configurable rule sets and decision trees, then returning a gate decision (approve, reject, challenge, escalate) with optional conditions (e.g., 'approve if amount < $5000 and risk_score < 40'). The gating engine applies policies in sequence, short-circuiting on hard blocks and accumulating soft constraints. Policies are defined declaratively (not in code) and can reference risk scores, user attributes, historical patterns, and external signals. Decisions include metadata (policy rule matched, confidence, remediation steps) for audit and debugging.
Unique: Policies are defined declaratively and evaluated server-side through MCP tools, decoupling policy logic from client applications. Supports conditional gating (not just binary approve/reject) and includes decision metadata for audit trails and debugging.
vs alternatives: Unlike hardcoded business logic in client applications, ActionGate's declarative policy engine allows non-technical stakeholders to modify rules without code changes. Compared to general-purpose rule engines (Drools, Easy Rules), ActionGate is optimized for transaction gating with built-in support for risk scores, user segmentation, and conditional actions.
Embeds ActionGate decision endpoints directly into MCP-based automation workflows, allowing orchestration systems (agents, workflow engines, CI/CD pipelines) to call risk evaluation, simulation, and gating tools as native steps. Decisions are returned synchronously with full context, enabling downstream workflow steps to branch based on gate outcomes (e.g., approve → process payment; reject → send decline notice; challenge → trigger 2FA). The integration is protocol-native (MCP tools), eliminating the need for custom API wrappers or polling loops.
Unique: Natively integrates as MCP tools, allowing any MCP-compatible workflow engine or agent to invoke decisions without custom adapters. Decisions are first-class workflow steps with full context propagation and branching support.
vs alternatives: Compared to REST-based decision APIs, ActionGate's MCP integration eliminates the need for custom HTTP clients and enables tighter coupling with agent reasoning loops. Compared to embedded decision libraries, MCP integration allows centralized policy management and decision auditing across distributed systems.
Evaluates risk across multiple independent dimensions (fraud risk, compliance risk, operational risk, customer lifetime value risk) by running parallel or sequential scoring models and aggregating results into a composite risk profile. Each dimension uses a specialized model (e.g., fraud detection uses gradient boosting; compliance uses rule-based scoring) and returns both a score and contributing factors. The system supports weighted aggregation, allowing different dimensions to contribute differently to the final decision. Scoring models are pluggable and can be swapped without changing client code.
Unique: Supports pluggable, independent risk models for different dimensions with configurable aggregation logic, enabling teams to mix rule-based and ML-based scoring without architectural changes. Returns per-dimension scores and factors, enabling explainability and debugging.
vs alternatives: Unlike monolithic fraud detection APIs that return a single score, ActionGate's multi-dimensional approach allows teams to understand and weight different risk types independently. Compared to building custom risk aggregation logic, ActionGate provides a standardized framework with audit trails.
Automatically logs all gating decisions (approve/reject/challenge) with full context (transaction details, risk scores, policy rules matched, timestamp, user ID) to an audit trail. Logs include decision metadata (confidence, contributing factors, alternative outcomes) enabling post-hoc analysis and compliance reporting. The audit trail is queryable and exportable, supporting regulatory requirements (PCI-DSS, GDPR, SOX) that mandate decision documentation. Logs are immutable (append-only) and include cryptographic signatures for tamper-evidence.
Unique: Audit logging is built into the decision engine (not a separate layer), ensuring every decision is logged with full context. Logs include decision metadata (confidence, factors) enabling root-cause analysis beyond simple approve/reject records.
vs alternatives: Compared to application-level logging (which is often incomplete or inconsistent), ActionGate's centralized audit trail ensures comprehensive coverage. Compared to generic audit frameworks, ActionGate's logs are optimized for decision analysis and compliance reporting.
Applies different policies and risk thresholds to different user segments (new vs returning customers, high-value vs low-value, geographic regions, risk tiers) by evaluating user attributes and historical behavior to determine segment membership, then routing to segment-specific policies. Segmentation logic is declarative and can reference user profile, transaction history, and external signals. Each segment has independent risk thresholds, approval rates, and challenge strategies, enabling tailored decision-making without duplicating core logic.
Unique: Segmentation is declarative and integrated into the policy engine, allowing segment-specific policies without code duplication. Segment membership is evaluated per transaction, enabling dynamic segmentation based on current user state.
vs alternatives: Compared to hardcoding segment logic in applications, ActionGate's declarative segmentation allows rapid policy changes. Compared to manual segment management, ActionGate's automated evaluation ensures consistency across decisions.
Routes transactions flagged as medium-risk to challenge workflows (additional verification steps like 2FA, identity verification, or manual review) instead of outright rejection. Challenge strategies are configurable per policy and can adapt based on risk score, user segment, and transaction context (e.g., high-value transactions require manual review; low-value require 2FA). The system tracks challenge outcomes (user completed verification, failed, abandoned) and feeds results back into risk models to improve future scoring. Challenge workflows are defined declaratively and can integrate with external verification providers (SMS, email, biometric).
Unique: Challenge workflows are first-class decision outcomes (not just approve/reject), with configurable strategies and outcome tracking. Challenge results feed back into risk models, creating a feedback loop for continuous improvement.
vs alternatives: Compared to static approve/reject decisions, ActionGate's challenge capability reduces false positives and improves user experience. Compared to manual challenge workflows, ActionGate's automation and outcome tracking enable data-driven optimization.
+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.
ActionGate scores higher at 44/100 vs Supabase at 42/100.
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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