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| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Walks the Git repository's file tree structure, respects .gitignore rules to filter out non-essential files, and aggregates source code and documentation into a single unified text document. Uses Git APIs or filesystem traversal to enumerate files while applying ignore patterns, then concatenates file contents with metadata markers (file paths, line counts) to preserve structure for LLM consumption.
Unique: Specifically optimized for LLM consumption by preserving file structure markers and respecting .gitignore patterns, rather than generic code indexing. Handles remote Git URLs directly without requiring local clones, reducing setup friction.
vs alternatives: Simpler and faster than cloning + custom scripts for codebase digestion, and more LLM-aware than generic tree-printing tools by formatting output for token efficiency
Clones or fetches Git repositories from remote sources (GitHub, GitLab, Gitea, Gitee, etc.) without requiring users to pre-clone locally. Supports shallow cloning (single branch, limited history) to minimize bandwidth and latency for large repositories. Uses Git CLI or libgit2 bindings to authenticate and fetch repository metadata and content.
Unique: Abstracts away Git CLI complexity and supports multiple Git hosting providers (GitHub, GitLab, Gitea, Gitee) with a unified interface, rather than requiring users to handle provider-specific authentication or URL formats.
vs alternatives: Faster than full clones for large repos due to shallow fetching, and more convenient than manual git clone commands for web-based or automated workflows
Allows users to define custom filtering rules beyond .gitignore (e.g., include only Python files, exclude files larger than 1MB, exclude test directories) via UI options, API parameters, or configuration files. Applies filters in addition to or instead of .gitignore rules, enabling fine-grained control over digest content.
Unique: Provides multiple filtering mechanisms (UI options, glob patterns, regex, file size limits) that compose with .gitignore rules, rather than relying solely on .gitignore.
vs alternatives: More powerful than .gitignore-only filtering because it enables language-specific, size-based, and pattern-based filtering without modifying repository files
Parses and applies .gitignore rules to exclude files from the digest, using pattern matching (wildcards, negations, directory-specific rules) consistent with Git's own ignore semantics. Implements gitignore spec compliance to avoid including build artifacts, node_modules, .env files, and other non-essential content that would bloat the LLM context.
Unique: Implements full gitignore spec compliance (including negation patterns and directory-specific rules) rather than simple glob matching, ensuring behavior matches Git's own filtering logic.
vs alternatives: More accurate than naive glob-based filtering because it respects gitignore semantics like negation patterns and directory scope, reducing risk of including unwanted files
Detects file types by extension and applies language-specific formatting (indentation, line breaks, comment markers) when aggregating code into the digest. Preserves syntax structure and readability for LLMs by maintaining code formatting, adding file path headers, and optionally including line numbers. Does not perform parsing or AST analysis — purely structural formatting for readability.
Unique: Preserves original code formatting and adds structural metadata (file paths, line numbers) specifically for LLM consumption, rather than reformatting code to a canonical style.
vs alternatives: More LLM-friendly than raw concatenation because it preserves context (file paths, line numbers) that helps LLMs understand code relationships and provide accurate suggestions
Estimates the token count of the generated digest using language model-specific tokenizers (e.g., tiktoken for OpenAI models) and provides warnings or truncation suggestions when the digest exceeds typical LLM context windows (4k, 8k, 16k, 128k tokens). May offer compression strategies (file filtering, summarization hints) to fit within token budgets.
Unique: Provides model-aware token estimation using language model-specific tokenizers, rather than generic character-to-token approximations, enabling accurate context window predictions.
vs alternatives: More accurate than character-count heuristics because it uses actual tokenizers, and more helpful than raw token counts by offering optimization suggestions
Processes multiple Git repositories in parallel or batch mode, generating digests for each and optionally combining them into a single multi-repository document. Uses concurrent fetching and processing to reduce total execution time compared to sequential ingestion. May support batch input formats (CSV, JSON) listing repository URLs.
Unique: Orchestrates parallel Git fetching and content aggregation across multiple repositories with coordinated rate limiting and error handling, rather than sequential processing.
vs alternatives: Significantly faster than sequential ingestion for 10+ repositories, and more robust than naive parallelization by handling rate limits and partial failures gracefully
Provides a web interface where users can paste or search for Git repository URLs, configure filtering options (file types, size limits, .gitignore respect), preview the generated digest, and download or copy it for LLM use. Offers real-time feedback on digest size, token count, and file inclusion decisions.
Unique: Provides a zero-setup web interface for repository ingestion, eliminating the need for CLI knowledge or local Git installation, with real-time preview and token counting.
vs alternatives: More accessible than CLI tools for non-technical users, and faster than manual cloning + custom scripts for one-off analyses
+3 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.
Supabase scores higher at 42/100 vs Gitingest at 25/100. Gitingest 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