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| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Scans local npm package.json and package-lock.json files to identify known security vulnerabilities in project dependencies using npm audit's vulnerability database. Integrates with MCP protocol to expose audit results as structured tool outputs that LLM agents can parse and act upon, enabling programmatic vulnerability detection without direct CLI invocation.
Unique: Exposes npm audit as an MCP tool endpoint, allowing LLM agents to invoke vulnerability scanning as a native capability within their reasoning loop rather than requiring shell command execution or separate API calls. Bridges the gap between CLI-based npm audit and agent-driven security workflows.
vs alternatives: Unlike running npm audit directly in CI/CD, this MCP server allows LLMs to interpret and act on audit results in real-time, enabling dynamic decision-making (e.g., 'block deployment if critical vulnerabilities found')
Audits npm dependencies in remote git repositories by cloning or fetching the repository, extracting package.json and package-lock.json, and running vulnerability scans without requiring local filesystem access. Implements repository URL parsing and temporary workspace management to support auditing third-party projects, enabling security assessment of external codebases through MCP protocol.
Unique: Implements repository cloning and temporary workspace management within the MCP server itself, abstracting away git operations from the LLM client. Allows agents to audit arbitrary public repositories by URL without needing git CLI knowledge or local repository setup.
vs alternatives: More flexible than static code scanning services because it runs npm audit (the authoritative npm vulnerability database) on actual dependency manifests, and integrates results directly into agent reasoning rather than requiring separate security tool integrations
Parses npm audit JSON output and transforms it into structured, agent-friendly metadata including vulnerability IDs, affected versions, severity classifications, and remediation paths. Implements schema-based extraction to normalize vulnerability data into consistent formats that LLM agents can reliably parse and reason about without additional parsing logic.
Unique: Implements deterministic schema-based extraction that produces consistent JSON structures across different npm versions and audit result variations, enabling reliable LLM parsing without fuzzy text extraction or regex fragility.
vs alternatives: More reliable than asking LLMs to parse raw npm audit CLI output because it provides pre-structured data with guaranteed schema, reducing hallucination risk and enabling deterministic agent decision-making
Wraps npm audit functionality as MCP tool endpoints that conform to the Model Context Protocol specification, enabling seamless integration with MCP-compatible clients (Claude, custom agents, etc.). Implements tool schema definition with input/output specifications, error handling, and response formatting that allows LLM clients to discover and invoke audit capabilities as native tools.
Unique: Implements full MCP server specification for audit tools, including tool schema definition, input validation, and response formatting. Allows LLM agents to discover audit capabilities through MCP's introspection mechanism rather than hardcoding tool definitions.
vs alternatives: More standardized than custom API wrappers because it uses the MCP protocol, enabling compatibility with any MCP-aware LLM client without building separate integrations for each platform
Filters and ranks vulnerability findings by severity level (critical, high, moderate, low) and enables agents to focus on high-impact issues first. Implements severity-based sorting and optional threshold filtering to allow LLM agents to make risk-aware decisions about which vulnerabilities require immediate action versus those that can be deferred.
Unique: Implements deterministic severity-based filtering that allows agents to make consistent risk decisions without requiring additional LLM inference steps. Severity thresholds are configurable, enabling different policies for different environments (dev vs production).
vs alternatives: More efficient than asking LLMs to prioritize vulnerabilities because filtering happens at the data layer before agent reasoning, reducing token usage and decision latency
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 @sunchao116/mcp-audit 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
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