| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes GitHub repository structure, file contents, and metadata through MCP's standardized Tools and Resources primitives, using the official GitHub REST API as the backend transport layer. The server implements JSON-RPC message routing to translate MCP tool invocations into authenticated GitHub API calls, with built-in pagination and error handling for large repositories. Supports both public and authenticated access patterns depending on provided credentials.
Unique: Official MCP server implementation that demonstrates the standard pattern for wrapping REST APIs (GitHub) into MCP's Tools and Resources model, using JSON-RPC transport to bridge LLM clients to GitHub's authentication and rate-limiting infrastructure
vs alternatives: As the official reference implementation, it establishes the canonical pattern for GitHub-MCP integration that other servers should follow, whereas custom implementations often lack proper error handling and authentication patterns
Implements MCP Tools that accept structured input (title, body, labels, assignees, milestones) and translate them into GitHub API POST requests to create issues and PRs. The server validates input schemas before submission and returns the created resource's full metadata including URL, number, and state. Supports templating and default values for common fields.
Unique: Wraps GitHub's issue/PR creation APIs with schema validation and structured metadata handling, allowing LLMs to generate properly-formatted GitHub artifacts without manual formatting or API knowledge
vs alternatives: Provides schema-based validation before API submission, preventing malformed requests and reducing failed API calls compared to direct API usage by LLMs
Implements MCP Tools for reading, writing, and deleting files in GitHub repositories with built-in conflict detection and merge simulation. The server supports creating commits with multiple file changes, validates file paths against repository structure, and can simulate merges to detect conflicts before attempting them. Supports both direct commits and pull request-based changes.
Unique: Integrates file operations with conflict detection and merge simulation, allowing LLMs to validate changes before committing rather than discovering conflicts after the fact
vs alternatives: Provides pre-flight conflict checking that prevents failed commits, whereas raw GitHub API would require the LLM to attempt commits and handle conflict errors reactively
Implements MCP tools for creating, updating, and listing GitHub webhooks with support for event filtering and payload configuration. Enables AI systems to subscribe to repository events (push, pull request, issue, etc.) and configure webhook delivery, supporting both HTTP POST and GitHub App event delivery mechanisms with automatic payload validation.
Unique: Exposes GitHub webhooks as MCP tools for event subscription and configuration, enabling LLM clients to set up event-driven automation without direct GitHub webhook API knowledge or manual configuration
vs alternatives: Provides webhook management through MCP versus manual GitHub UI configuration, with automatic event type validation and payload configuration making it easier for AI systems to subscribe to repository events
Exposes MCP Tools for creating, deleting, and listing branches, with built-in validation that checks for naming conflicts and protected branch rules before attempting operations. The server queries GitHub's branch protection settings and returns detailed status including whether a branch is protected, has required status checks, or is the default branch. Supports both simple branch creation from HEAD and creation from arbitrary commit SHAs.
Unique: Integrates GitHub's branch protection API to provide LLMs with visibility into branch safety constraints before attempting operations, preventing failed automation due to protection rules
vs alternatives: Proactively checks branch protection status and returns detailed constraint information, whereas direct git/GitHub API usage would fail silently or require separate queries
Implements MCP Tools that translate natural language or structured search queries into GitHub's advanced search syntax (using qualifiers like language:, stars:, created:, etc.), execute searches via the GitHub Search API, and return ranked results with relevance metadata. The server handles pagination and result deduplication, supporting searches across code, issues, pull requests, and repositories. Results include context snippets and match highlighting.
Unique: Abstracts GitHub's search syntax complexity by accepting natural language or structured parameters and translating them into optimized search queries, with built-in result ranking and deduplication
vs alternatives: Provides a simplified interface to GitHub Search API that LLMs can use without learning search syntax, whereas raw API usage requires the LLM to construct complex query strings
Exposes MCP Tools that retrieve commit history for files or branches, fetch full commit diffs, and provide semantic context about changes (files modified, lines added/removed, commit message parsing). The server supports filtering by author, date range, and commit message patterns. Diffs are returned in unified format with optional syntax highlighting context for code changes.
Unique: Combines GitHub's commit and diff APIs with semantic parsing to extract change context (files modified, impact summary) that helps LLMs understand code evolution without manually parsing diffs
vs alternatives: Provides structured commit metadata and semantic change summaries alongside raw diffs, whereas raw git/GitHub API returns only unstructured diff text
Implements MCP Tools for submitting PR reviews (approve, request changes, comment), retrieving PR review status and reviewer assignments, and checking merge eligibility based on required status checks and review requirements. The server validates review state transitions and returns detailed PR status including CI/CD check results, required reviewers, and merge conflict status.
Unique: Integrates PR review submission with merge eligibility checking, allowing LLMs to understand both the review process and the broader merge constraints (required checks, branch protection rules)
vs alternatives: Provides holistic PR status visibility including review state, CI results, and merge eligibility in a single query, whereas separate API calls would require the LLM to correlate multiple responses
+4 more capabilities
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.
GitHub MCP Server scores higher at 60/100 vs Supabase at 42/100.
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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
+1 more capabilities