Supabase ranks higher at 42/100 vs Email Deliverability Audit — SPF/DKIM/DMARC Check at 32/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Email Deliverability Audit — SPF/DKIM/DMARC Check | Supabase |
|---|---|---|
| Type | MCP Server | MCP Server |
| UnfragileRank | 32/100 | 42/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability checks the SPF (Sender Policy Framework) records of a specified domain to ensure that the domain's email servers are authorized to send emails on behalf of that domain. It uses DNS queries to retrieve the SPF record and validates it against the sending IP address. The implementation leverages a robust DNS resolver to ensure accurate and timely responses, making it distinct in its reliability and speed.
Unique: Utilizes a high-performance DNS resolver optimized for quick SPF record lookups, ensuring minimal latency.
vs alternatives: More efficient than traditional SPF checkers due to its optimized DNS querying mechanism.
This capability verifies the DKIM (DomainKeys Identified Mail) selector for a domain by querying the DNS for the DKIM record associated with the specified selector. It checks if the DKIM signature can be validated against the public key provided in the DNS record. The approach ensures that the selector is properly configured and that the DKIM signing process is functioning as intended.
Unique: Incorporates a systematic approach to validate DKIM selectors, ensuring comprehensive checks against multiple selectors if specified.
vs alternatives: More thorough than basic DKIM checkers by allowing multiple selector validations in a single query.
This capability analyzes the DMARC (Domain-based Message Authentication, Reporting & Conformance) policy of a domain by retrieving the DMARC record from DNS. It evaluates the policy's alignment with SPF and DKIM records and provides insights into the effectiveness of the domain's email authentication strategy. The implementation uses a structured parsing method to interpret DMARC policies accurately.
Unique: Utilizes a detailed policy parsing algorithm to provide actionable insights based on DMARC configurations and their implications.
vs alternatives: Offers deeper analysis compared to standard DMARC checkers by evaluating policy effectiveness against existing SPF and DKIM records.
This capability checks the health of the MX (Mail Exchange) records for a domain by querying DNS for the MX records and verifying their correctness and availability. It assesses whether the mail servers listed are reachable and properly configured to handle incoming emails. The method includes a connectivity test to ensure that the MX servers are responsive.
Unique: Combines DNS querying with a connectivity test to provide a comprehensive assessment of MX record health.
vs alternatives: More reliable than basic MX record checkers by including server response verification.
This capability calculates a composite deliverability score for a domain based on the results of SPF, DKIM, DMARC, and MX record checks. It aggregates these results into a score from 0 to 100, providing a quick reference for the overall email deliverability health of the domain. The scoring algorithm is designed to weigh each factor according to its impact on deliverability.
Unique: Employs a unique scoring algorithm that dynamically adjusts weights based on industry best practices for email deliverability.
vs alternatives: Provides a more nuanced score compared to generic deliverability tools by incorporating multiple authentication checks.
This capability generates prioritized recommendations for fixing issues identified during the email deliverability audit. It analyzes the results of SPF, DKIM, DMARC, and MX checks to suggest actionable steps to improve email authentication and deliverability. The recommendations are ranked based on their potential impact and ease of implementation.
Unique: Utilizes a decision-tree approach to prioritize recommendations based on severity and implementation complexity, ensuring actionable insights.
vs alternatives: More tailored than generic recommendation engines by focusing specifically on email authentication issues.
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 Email Deliverability Audit — SPF/DKIM/DMARC Check at 32/100.
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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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