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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes SPM's native dependency resolution engine through the Model Context Protocol, allowing Claude and other MCP clients to query package metadata, resolve version constraints, and inspect dependency graphs without executing shell commands. Implements MCP server protocol in Swift to bridge SPM's internal package resolution APIs with LLM-based tools, enabling structured queries about package compatibility and transitive dependencies.
Unique: Native Swift implementation of MCP server that directly integrates with SPM's internal package resolution APIs rather than wrapping shell commands, enabling structured, type-safe queries about package dependencies without subprocess overhead or parsing fragility
vs alternatives: Provides direct programmatic access to SPM's dependency resolver within Claude conversations, whereas alternatives require shell command execution or external REST APIs, reducing latency and enabling richer structured responses
Implements the Model Context Protocol specification as a native Swift server, handling JSON-RPC message serialization, request routing, and resource/tool registration. Uses Swift's async/await concurrency model to manage bidirectional communication with MCP clients, providing a type-safe foundation for exposing SPM capabilities through standardized MCP endpoints (resources, tools, prompts).
Unique: Implements MCP server protocol natively in Swift using async/await concurrency primitives, avoiding the overhead of spawning separate processes or managing thread pools, and providing type-safe message handling through Swift's Codable framework
vs alternatives: More efficient than Python or Node.js MCP servers for Swift-specific operations because it eliminates language boundary crossing and leverages Swift's compile-time type safety for protocol message validation
Parses Swift Package Manager manifest files (Package.swift) to extract structured metadata including dependencies, targets, products, and build settings. Converts unstructured manifest code into queryable data structures that can be inspected by LLM clients, enabling semantic understanding of package configuration without manual file parsing or regex-based extraction.
Unique: Leverages Swift's native AST parsing capabilities (via SwiftSyntax or direct SPM APIs) to extract manifest structure with full semantic understanding, rather than regex-based or line-by-line parsing, enabling accurate handling of complex manifest configurations
vs alternatives: Provides accurate, semantically-aware manifest parsing compared to regex-based tools, and avoids the fragility of shell-based parsing (e.g., swift package describe) by working directly with SPM's internal data structures
Resolves version constraints specified in package dependencies against available package versions, determining which versions satisfy all constraints and detecting conflicts. Implements SPM's constraint resolution algorithm (similar to semantic versioning resolution) to answer compatibility queries, enabling LLM clients to understand which package versions can coexist in a project.
Unique: Integrates SPM's native constraint resolution algorithm directly, providing the same resolution logic that Xcode uses, rather than reimplementing a separate resolver that may diverge from SPM's behavior
vs alternatives: Guarantees compatibility with SPM's actual resolution behavior because it uses the same underlying algorithm, whereas external resolvers (e.g., custom Python scripts) may produce different results due to algorithm differences
Builds and traverses the complete transitive dependency graph for a Swift package, enabling queries about indirect dependencies, circular dependency detection, and dependency depth analysis. Implements graph traversal algorithms (BFS/DFS) to compute dependency metrics and identify structural issues in the dependency tree.
Unique: Provides direct access to SPM's internal dependency graph representation, enabling efficient traversal without reconstructing the graph from manifest files, and supporting both forward and reverse dependency queries
vs alternatives: More efficient than parsing manifests and reconstructing graphs manually because it leverages SPM's pre-computed graph structure, and provides accurate cycle detection that accounts for SPM's resolution semantics
Queries package metadata from the Swift Package Index and other registries, retrieving information such as package description, license, repository URL, maintainer information, and available versions. Implements HTTP-based registry queries with caching to reduce network overhead and provide fast metadata lookups for LLM clients.
Unique: Integrates directly with Swift Package Index and SPM registry APIs, providing authoritative metadata without relying on third-party package databases, and implementing intelligent caching to balance freshness with performance
vs alternatives: Provides more accurate and up-to-date metadata than manual registry searches because it queries official sources directly, and caching reduces latency compared to repeated HTTP requests
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 spm-mcp at 20/100.
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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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