| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Implements the Model Context Protocol (MCP) server specification, exposing security scanning and code analysis capabilities through a standardized interface that LLM clients can discover and invoke. Uses MCP's resource and tool registration patterns to advertise available security operations, handle JSON-RPC message routing, and manage bidirectional communication with MCP-compatible clients (Claude, custom LLM applications). Enables seamless integration of Aikido's security analysis into AI-driven development workflows without custom API bindings.
Unique: Implements MCP server pattern specifically for security scanning, allowing LLMs to invoke Aikido's analysis through standardized protocol rather than custom API wrappers, reducing integration friction for MCP-native environments
vs alternatives: Provides native MCP integration for Aikido security scanning, whereas direct REST API integration requires custom client code and lacks MCP's standardized discovery and error handling
Performs static analysis on code to identify security vulnerabilities, misconfigurations, and code quality issues. Likely uses AST parsing, pattern matching, or rule-based engines to scan code without execution. Integrates with MCP to expose findings as structured tool outputs that LLMs can reason about and suggest fixes for. Supports multiple languages and vulnerability categories (injection, authentication, data exposure, etc.).
Unique: unknown — insufficient data on whether Aikido uses proprietary rule engines, open-source SAST tools, or ML-based detection; specific analysis approach not documented
vs alternatives: Integrated into MCP ecosystem, allowing LLMs to invoke security scanning natively, whereas standalone SAST tools (SonarQube, Semgrep) require separate CI/CD integration and manual result interpretation
Registers security scanning and analysis tools with the MCP protocol's tool registry, allowing MCP clients to discover available operations, their parameters, and expected outputs through introspection. Implements MCP's tools/list and tools/call endpoints to advertise capabilities (e.g., 'scan_code', 'check_dependencies') with JSON schemas defining input parameters and return types. Enables LLM clients to dynamically understand what security operations are available without hardcoded knowledge.
Unique: Implements MCP's standardized tool registration pattern, allowing security tools to be discovered and invoked through a protocol-agnostic interface rather than custom API documentation
vs alternatives: Provides standardized tool discovery via MCP, whereas custom REST APIs require manual documentation and client-side schema management
Scans project dependencies (npm packages, Python libraries, etc.) for known vulnerabilities using vulnerability databases (likely CVE, npm audit, or similar sources). Analyzes dependency trees to identify transitive vulnerabilities and outdated packages. Exposes findings through MCP tools so LLMs can recommend dependency updates or alternative packages. May include license compliance checking and supply chain risk assessment.
Unique: unknown — insufficient data on whether Aikido uses npm audit, Snyk, or proprietary vulnerability database; specific dependency scanning approach not documented
vs alternatives: Integrated into MCP workflow, allowing LLMs to recommend dependency updates directly, whereas npm audit or Snyk require separate CLI invocation and manual result parsing
Scans code and configuration files for exposed secrets (API keys, database credentials, tokens), misconfigurations, and hardcoded sensitive data. Uses pattern matching, entropy analysis, or regex rules to detect common secret formats (AWS keys, private keys, database URLs). Integrates with MCP to expose findings as tools that LLMs can use to identify and remediate secret exposure. May support multiple configuration formats (YAML, JSON, .env files).
Unique: unknown — insufficient data on whether Aikido uses truffleHog, detect-secrets, or proprietary pattern matching; specific secret detection approach not documented
vs alternatives: Integrated into MCP workflow, allowing LLMs to identify and remediate secrets in real-time, whereas standalone tools (git-secrets, truffleHog) require separate CI/CD integration
Analyzes code for quality issues, style violations, and deviations from best practices (e.g., unused variables, complex functions, missing error handling). Uses AST analysis or linting rules to identify code smells and maintainability issues. Exposes findings through MCP tools so LLMs can suggest refactoring or improvements. May integrate with language-specific linters (ESLint, Pylint, etc.) or use proprietary rules.
Unique: unknown — insufficient data on whether Aikido uses existing linters, custom AST analysis, or ML-based quality detection; specific approach not documented
vs alternatives: Integrated into MCP workflow for real-time quality feedback via LLM, whereas standalone linters (ESLint, Pylint) require separate configuration and manual result interpretation
Manages MCP resource lifecycle including file access, caching, and context passing between client and server. Implements MCP's resources/list and resources/read endpoints to expose code files, configuration, and analysis results as accessible resources. Handles resource URI schemes, caching strategies, and memory management to efficiently serve large codebases to LLM clients. May implement incremental scanning or result caching to reduce latency.
Unique: Implements MCP resource pattern for security analysis context, allowing efficient code access and caching without requiring full codebase transmission to LLM clients
vs alternatives: Uses MCP's resource protocol for efficient context management, whereas custom APIs require manual caching and context optimization logic
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 @aikidosec/mcp at 22/100. @aikidosec/mcp 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