| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Parses and analyzes MCP (Model Context Protocol) server tool definitions to extract schema, parameters, and capabilities without executing the server. Uses AST-like traversal of tool manifests to build a semantic model of available functions, their input/output contracts, and permission requirements for downstream security evaluation.
Unique: Purpose-built for MCP protocol semantics rather than generic API scanning; understands MCP-specific tool metadata patterns and integrates with MCP server lifecycle
vs alternatives: Specialized for MCP servers vs. generic API security scanners that lack MCP protocol awareness and context-specific risk patterns
Evaluates extracted tool definitions against a configurable risk taxonomy (likely OWASP-aligned or custom policy rules) to assign severity scores and risk categories. Implements pattern matching on tool names, parameters, and descriptions to detect high-risk operations (file system access, network calls, credential handling) and generates a scored risk report for policy decision-making.
Unique: Integrates SINT (Security Intent) framework for MCP-specific risk patterns; likely includes rules for common dangerous MCP tool patterns (e.g., arbitrary code execution, credential exposure via tool parameters)
vs alternatives: Purpose-built risk taxonomy for MCP tools vs. generic API security scoring that doesn't understand agent-specific threat models
Implements a policy evaluation engine that takes risk classifications and applies configurable allow/deny/require-approval rules to determine whether an LLM agent should be permitted to call a specific tool. Supports policy composition (e.g., 'block all file system tools', 'require approval for network calls') and integrates with MCP server request interception to enforce decisions at runtime.
Unique: Integrates directly with MCP server request pipeline for real-time gating; supports context-aware policies (agent identity, user role, tool category) rather than static blocklists
vs alternatives: Operates at MCP protocol layer for native integration vs. external proxy-based gating that adds latency and requires protocol translation
Validates tool invocation parameters against extracted MCP tool schemas to detect parameter injection, type mismatches, and constraint violations before execution. Implements JSON schema validation with custom rules for dangerous parameter patterns (e.g., shell metacharacters in command parameters, file paths outside allowed directories) and generates detailed validation reports.
Unique: Combines JSON schema validation with MCP-specific parameter risk patterns; includes built-in rules for common injection vectors in agent tool calls (shell metacharacters, path traversal, SQL injection signatures)
vs alternatives: MCP-native validation vs. generic JSON schema validators that lack agent-specific threat context and injection pattern detection
Records all tool access decisions (allowed, denied, approved) with context (agent identity, user, timestamp, tool name, parameters, risk classification) to an audit log. Generates compliance reports summarizing tool usage patterns, policy violations, and high-risk tool invocations for security review and regulatory compliance (SOC 2, HIPAA, etc.).
Unique: Integrates audit logging directly into MCP request pipeline; captures full context (agent identity, parameters, risk score, policy decision) in structured format for compliance and forensic analysis
vs alternatives: Native MCP integration for complete audit trail vs. external logging that may miss context or require manual correlation of events
Provides a rule engine for defining custom risk classification and access control policies using a declarative configuration format (likely YAML or JSON DSL). Supports rule composition, conditional logic (e.g., 'block tool X if parameter Y contains Z'), and integration with external policy sources. Enables teams to define organization-specific security policies without code changes.
Unique: Declarative rule engine designed for MCP-specific threat patterns; supports context-aware rules (agent identity, tool category, parameter content) without requiring code changes
vs alternatives: Declarative policy configuration vs. hard-coded policies that require code changes and redeployment for policy updates
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 @pshkv/mcp-scanner at 23/100. @pshkv/mcp-scanner 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
+1 more capabilities