| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Intercepts all outbound MCP tool invocations at the protocol level before execution, applies configurable security policies (allowlists, denylists, parameter validation rules), and either permits or blocks execution based on policy match. Uses a proxy middleware pattern that sits between the MCP client and server, inspecting the tool name, parameters, and execution context against a declarative policy ruleset.
Unique: Operates as an MCP protocol-level proxy rather than application-level wrapper, enabling transparent interception of all tool calls without modifying client or server code. Uses declarative policy rules that can express complex conditions (tool name patterns, parameter constraints, context-based rules) in a single configuration file.
vs alternatives: Provides MCP-native security enforcement without requiring changes to existing MCP clients or servers, whereas generic API gateway solutions lack MCP protocol awareness and require custom integration per tool.
Analyzes tool parameters and execution context for indicators of prompt injection attacks (e.g., suspicious patterns in string parameters that attempt to override tool behavior or escape context). Uses pattern matching, heuristic analysis, or optional integration with LLM-based classifiers to detect malicious payloads and either sanitize parameters or block execution. Operates on the parameter values before they reach the underlying tool implementation.
Unique: Specifically targets MCP tool parameters rather than generic prompt content, using tool-aware detection rules that understand the semantics of different parameter types (file paths, SQL, shell commands, etc.). Can integrate with optional LLM classifiers for context-aware detection while maintaining fast heuristic fallbacks.
vs alternatives: More precise than generic prompt injection filters because it understands MCP tool semantics and parameter context, whereas general-purpose content filters treat all text equally and miss tool-specific attack patterns.
Validates all tool call parameters against strict schemas before execution, ensuring parameters match expected types, formats, ranges, and constraints. Uses JSON Schema or similar declarative validation rules to reject malformed or out-of-bounds parameters that could cause tool misbehavior or security issues. Validation happens synchronously at the proxy layer, blocking invalid calls before they reach the tool implementation.
Unique: Applies declarative JSON Schema validation at the MCP protocol boundary, enabling schema-driven security without modifying tool implementations. Supports custom validation rules and coercion strategies that can normalize parameters (e.g., path canonicalization) before passing to tools.
vs alternatives: More flexible and maintainable than hardcoded validation in each tool because schemas are centralized and can be updated without redeploying tools, whereas per-tool validation requires changes across multiple codebases.
Enforces fine-grained access control rules based on execution context (caller identity, tool name, parameter values, execution environment, time-based policies). Uses a context evaluation engine that matches incoming tool calls against rules like 'allow tool X only if caller is admin' or 'block file deletion after business hours'. Rules are expressed declaratively and evaluated synchronously at the proxy layer before tool execution.
Unique: Evaluates access control rules against rich execution context (caller identity, environment, time) rather than just tool names, enabling policies that express 'who can call what when'. Uses a declarative rule engine that can combine multiple context attributes in a single policy.
vs alternatives: More expressive than simple allowlist/denylist approaches because it can encode context-dependent policies, whereas basic tool allowlists cannot distinguish between different callers or execution environments.
Logs all tool calls (allowed and blocked) with full context including caller identity, tool name, parameters, decision reason, timestamp, and execution result. Stores logs in a structured format (JSON) that can be queried, analyzed, and exported for compliance audits. Integrates with optional external logging systems (e.g., Datadog, Splunk) via standard log sinks. Provides request tracing IDs to correlate tool calls across distributed systems.
Unique: Captures complete tool call lifecycle (request, decision, execution, result) in structured logs with request tracing IDs, enabling end-to-end audit trails. Supports multiple log sinks (local, cloud, external services) and can redact sensitive data based on configurable rules.
vs alternatives: More comprehensive than application-level logging because it captures all tool calls at the protocol boundary regardless of tool implementation, whereas per-tool logging requires changes to each tool and may miss calls.
Enforces rate limits on tool calls to prevent abuse, DoS attacks, or resource exhaustion. Supports multiple rate limiting strategies (per-caller, per-tool, per-caller-per-tool, time-window based) and can apply different limits based on execution context. Uses token bucket or sliding window algorithms to track call rates and reject calls that exceed configured limits. Provides configurable backoff strategies and quota reset policies.
Unique: Applies rate limiting at the MCP protocol layer with context-aware rules (per-caller, per-tool, per-context), enabling fine-grained quota enforcement. Supports multiple rate limiting algorithms and can integrate with distributed state stores for multi-instance deployments.
vs alternatives: More flexible than generic API rate limiting because it understands MCP tool semantics and can apply different limits per tool and caller, whereas generic API gateways apply uniform limits across all endpoints.
Provides a declarative configuration format (JSON/YAML) for defining all security policies (allowlists, denylists, parameter validation, access control, rate limits) in a single place. Policies are version-controlled, auditable, and can be updated without code changes. Includes schema validation for policy definitions and provides clear error messages for misconfiguration. Supports policy composition and inheritance to reduce duplication.
Unique: Centralizes all MCP security policies in a single declarative configuration file with schema validation, enabling version control and audit trails. Supports policy composition and inheritance to reduce duplication across multiple tools and rules.
vs alternatives: More maintainable than scattered security logic across multiple tools because policies are centralized and version-controlled, whereas per-tool security requires changes across multiple codebases and lacks a single source of truth.
Integrates with external identity providers (OAuth2, SAML, OIDC) and authorization systems (RBAC, ABAC, policy engines) to make access control decisions based on external context. Supports token validation, role/attribute lookup, and delegation to external policy engines. Caches identity and authorization data to minimize latency and external service dependencies. Provides hooks for custom authorization logic via pluggable adapters.
Unique: Provides pluggable adapters for common identity providers (OAuth2, SAML, OIDC) and authorization systems, with built-in caching to minimize external service latency. Supports delegation to external policy engines for complex authorization logic.
vs alternatives: Enables MCP security to leverage existing enterprise identity and authorization infrastructure, whereas standalone MCP security requires separate identity management and cannot integrate with organization-wide access control systems.
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 @aiclude/mcp-guard at 24/100. @aiclude/mcp-guard leads on ecosystem, while Supabase is stronger on quality.
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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
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