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| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Analyzes MCP server repositories from GitHub URLs or local file uploads to extract security metrics and risk assessments. The system performs automated security scoring across repository content, likely scanning for common vulnerabilities, dependency issues, and code quality indicators. Results are delivered as numeric security scores and risk classifications within claimed sub-10-second latency, enabling rapid security vetting of MCP implementations before integration.
Unique: Specialized security analysis pipeline for MCP server repositories, likely incorporating MCP-specific vulnerability patterns (e.g., unsafe tool definitions, unvalidated function schemas, improper context handling) rather than generic code scanning. Supports both remote GitHub analysis and local file uploads, enabling offline security assessment of MCP implementations.
vs alternatives: Faster and more targeted than manual GitHub security audits or generic SAST tools because it understands MCP-specific threat models (tool invocation safety, schema validation, context isolation) rather than treating MCPs as generic Python/TypeScript projects.
Extracts quantitative GitHub statistics from MCP repositories including star count, fork count, and activity scores. The system queries GitHub repository metadata to surface adoption and maintenance signals, enabling comparative analysis of MCP popularity and community engagement. Metrics are returned as structured numeric values, supporting ranking and filtering of MCPs by community traction.
Unique: Specialized metrics extraction for MCP repositories, likely incorporating MCP-specific activity signals (e.g., tool definition updates, schema changes, integration test additions) beyond generic GitHub metrics. Enables rapid comparative analysis of MCP ecosystem health without manual GitHub browsing.
vs alternatives: More efficient than manually checking GitHub profiles for each MCP because it aggregates adoption signals in a single query, and potentially more meaningful than generic GitHub metrics because it may weight MCP-specific signals (e.g., tool schema stability, test coverage for tool invocation).
Processes up to 4 MCP repositories in a single analysis session, accepting both GitHub URLs and local file uploads (ZIP archives or folder structures) as input sources. The system normalizes heterogeneous input formats into a unified analysis pipeline, enabling comparative security and metrics assessment across repositories from different sources without requiring separate analysis runs. Results are aggregated and returned within claimed sub-10-second latency.
Unique: Unified batch analysis pipeline that normalizes heterogeneous input sources (GitHub URLs, local ZIP uploads, folder structures) into a single security and metrics assessment workflow. Likely uses a common internal representation for MCP repositories regardless of source, enabling fair comparative analysis across public and private implementations.
vs alternatives: More efficient than sequential single-repository analysis because it processes up to 4 MCPs in parallel, and more flexible than GitHub-only tools because it supports local file uploads for proprietary or pre-release MCP implementations.
Provides read-only access to a pre-analyzed directory of thousands of MCP repositories, organized by category (e.g., 'Productivity MCPs'). The system maintains an indexed database of analyzed MCPs, enabling rapid browsing and filtering without triggering on-demand analysis. Users can explore the directory via category-based navigation, discovering MCPs by functional domain rather than searching by name or URL.
Unique: Curated, pre-indexed MCP directory with category-based organization, enabling rapid discovery without GitHub searching. Likely maintains cached analysis results for thousands of MCPs, reducing latency compared to on-demand analysis. Category taxonomy appears MCP-specific (e.g., 'Productivity') rather than generic GitHub project categories.
vs alternatives: Faster and more discoverable than raw GitHub search because MCPs are pre-analyzed and organized by functional domain, and more curated than GitHub's generic repository listing because it filters specifically for MCP implementations.
Performs on-demand analysis of MCP repositories with claimed sub-10-second turnaround time, supporting both GitHub URLs and local file uploads. The system likely uses optimized analysis pipelines (possibly parallel processing of security scanning and metrics extraction) to achieve rapid results. Analysis is non-blocking and returns results asynchronously, enabling interactive exploration of MCP repositories without long wait times.
Unique: Optimized analysis pipeline designed for sub-10-second turnaround on MCP repositories, likely using parallel processing of security scanning and metrics extraction, and possibly caching of GitHub API results. Supports both remote and local input sources without requiring separate analysis paths.
vs alternatives: Faster than manual GitHub audits or sequential analysis tools because it parallelizes security and metrics extraction, and more responsive than batch-oriented analysis systems because it prioritizes interactive latency over throughput.
Identifies security risks specific to MCP implementations, likely scanning for unsafe tool definitions, unvalidated function schemas, improper context isolation, and other MCP-specific threat patterns. The system applies domain-specific security rules tailored to MCP architecture (tool invocation safety, schema validation, resource access controls) rather than generic code vulnerability scanning. Security findings are aggregated into a numeric score and risk classification.
Unique: Domain-specific security analysis tailored to MCP threat models, likely detecting unsafe tool definitions, schema validation gaps, and context isolation failures that generic SAST tools would miss. Incorporates MCP-specific security patterns (e.g., tool invocation safety, function schema validation, resource access controls) rather than generic code vulnerabilities.
vs alternatives: More relevant than generic code security scanners because it understands MCP-specific threat models (tool invocation safety, schema validation, context isolation), and more targeted than manual security audits because it automates detection of common MCP security anti-patterns.
Enables analysis of MCP repositories from local file uploads (ZIP archives or folder structures) without requiring GitHub URLs or public repository access. The system accepts local file inputs, normalizes them into a standard MCP representation, and applies the same security and metrics analysis pipeline as GitHub-based analysis. This capability supports analysis of proprietary, pre-release, or private MCP implementations that are not publicly available on GitHub.
Unique: Supports analysis of non-public MCP implementations via local file uploads, enabling security assessment of proprietary and pre-release MCPs without GitHub dependency. Normalizes heterogeneous file formats (ZIP, folders) into a unified analysis pipeline, supporting both public and private MCP evaluation workflows.
vs alternatives: More flexible than GitHub-only analysis tools because it supports proprietary and pre-release MCPs, and more private than cloud-based analysis services because local uploads are not indexed or shared in the public directory.
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 MCP Hunt at 23/100. MCP Hunt leads on quality, while Supabase is stronger on ecosystem. Supabase also has a free tier, making it more accessible.
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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