| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Intercepts and validates AI agent actions before execution by implementing a middleware layer that inspects tool calls, API requests, and state mutations against configurable security policies. Uses a hook-based architecture to wrap agent execution pipelines, enabling real-time inspection of intent, parameters, and side effects without modifying core agent logic.
Unique: Implements action interception at the middleware layer rather than post-hoc monitoring, enabling preventive blocking before agents execute dangerous operations. Uses declarative policy definitions that can be composed and reused across multiple agents without code changes.
vs alternatives: Provides real-time action blocking before execution (not just logging after), whereas most agent monitoring tools only audit completed actions retroactively
Validates tool/function calls against JSON schemas and enforces parameter constraints (type, range, format, allowlists) before agents invoke external APIs or tools. Implements schema-aware validation that checks not just type correctness but also business logic constraints like rate limits, resource quotas, and parameter dependencies.
Unique: Combines JSON schema validation with business logic constraint enforcement in a single pipeline, allowing declarative definition of both type safety and domain-specific rules (quotas, allowlists, dependencies) without custom code per tool.
vs alternatives: Goes beyond simple type checking to enforce business constraints like rate limits and resource quotas, whereas standard JSON schema validation only checks structure and type
Monitors agent execution patterns and detects anomalous behavior by tracking metrics like action frequency, resource consumption, error rates, and decision patterns over time. Uses statistical baselines and rule-based heuristics to identify deviations that may indicate agent malfunction, adversarial prompting, or security incidents.
Unique: Implements continuous behavior monitoring with statistical baseline comparison rather than static rule-based detection, enabling detection of subtle deviations that fixed rules would miss. Tracks multi-dimensional metrics (frequency, latency, error rate, resource consumption) to build composite anomaly scores.
vs alternatives: Detects behavioral anomalies through statistical analysis of execution patterns, whereas simple rule-based monitoring only catches explicit policy violations
Enforces fine-grained access control by binding agents to specific resources, APIs, and capabilities based on identity, role, or context. Implements a capability-based security model where agents receive a scoped set of allowed tools and resources, with enforcement at the invocation layer preventing access to unbound capabilities.
Unique: Uses capability-based security model where agents receive explicit grants of allowed tools rather than checking permissions at invocation time, enabling efficient enforcement and clear visibility into agent capabilities. Supports context-aware binding where capabilities can vary based on tenant, user, or execution context.
vs alternatives: Implements capability-based security (explicit grants) rather than permission-based (implicit allows), providing stronger isolation guarantees and clearer audit trails
Detects and mitigates prompt injection attacks by analyzing user inputs and agent prompts for suspicious patterns, embedded instructions, or attempts to override system prompts. Uses pattern matching, semantic analysis, and heuristics to identify injection attempts before they reach the LLM, with optional sanitization or rejection of suspicious inputs.
Unique: Implements multi-layered injection detection combining pattern matching for known attack vectors with heuristic analysis for novel attempts, rather than relying on a single detection method. Can operate in detection-only mode (logging) or enforcement mode (blocking/sanitizing).
vs alternatives: Provides proactive injection detection before inputs reach the LLM, whereas most agent security focuses on output filtering after the LLM has already processed potentially malicious inputs
Filters and moderates agent outputs before they are returned to users or trigger external actions, checking for harmful content, sensitive data leakage, policy violations, or format violations. Implements a moderation pipeline that can reject, sanitize, or flag outputs based on configurable rules and optional integration with content moderation APIs.
Unique: Implements post-generation output filtering with multiple moderation strategies (pattern-based, API-based, custom rules) that can be composed and weighted, rather than relying on a single moderation approach. Supports both rejection and sanitization modes.
vs alternatives: Provides comprehensive output moderation including data leakage detection and policy compliance checking, whereas most agent security focuses primarily on harmful content filtering
Records comprehensive audit logs of all agent actions, decisions, and security events with immutable storage and compliance-ready reporting. Captures action details (what, who, when, why), security decisions (approved/rejected, reason), and context (user, tenant, resource) in a structured format suitable for compliance audits and forensic analysis.
Unique: Implements structured audit logging with compliance-ready reporting, capturing not just actions but also security decisions and context in a format suitable for regulatory audits. Supports multiple log destinations and formats for integration with compliance tools.
vs alternatives: Provides compliance-focused audit logging with structured data and reporting, whereas generic application logging typically lacks the compliance context and formatting needed for regulatory audits
Enforces rate limits and resource quotas on agent actions to prevent abuse, resource exhaustion, and uncontrolled costs. Implements multiple rate-limiting strategies (token bucket, sliding window, quota-based) with per-agent, per-user, or per-resource granularity, with configurable thresholds and backoff behavior.
Unique: Implements flexible rate limiting with multiple strategies (token bucket, sliding window, quota-based) and granular scoping (per-agent, per-user, per-resource), allowing fine-tuned control over agent resource consumption. Supports both hard limits (rejection) and soft limits (backoff/throttling).
vs alternatives: Provides multi-strategy rate limiting with granular scoping, whereas most agent frameworks only support simple per-agent rate limits without resource-level or cost-based control
+2 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 agenshield at 26/100.
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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