Supabase ranks higher at 42/100 vs Claude-File-Recovery, recover files from your ~/.claude sessions at 33/100. Capability-level comparison backed by match graph evidence from real search data.
| Feature | Claude-File-Recovery, recover files from your ~/.claude sessions | Supabase |
|---|---|---|
| Type | CLI Tool | MCP Server |
| UnfragileRank | 33/100 | 42/100 |
| Adoption |
| 1 |
| 0 |
| Quality | 0 | 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Scans the ~/.claude directory structure to locate and parse serialized conversation session files, extracting embedded file artifacts that were generated or uploaded during Claude interactions. Uses filesystem traversal to identify session metadata and deserializes session state to recover file references and content payloads that may have been lost or deleted from the user's working directory.
Unique: Directly targets Claude's local session storage format to recover artifacts that exist nowhere else — most recovery tools focus on cloud backups or trash bins, but this exploits the fact that Claude caches full conversation state locally including all generated files
vs alternatives: Recovers Claude-specific artifacts that generic file recovery tools cannot access because they're embedded in proprietary session serialization rather than stored as independent files
Recursively walks the ~/.claude directory tree to build an index of all session files, extracting metadata like creation timestamps, conversation IDs, and file references without loading entire session payloads into memory. Uses efficient filesystem scanning to catalog available sessions and their contents, enabling users to selectively recover files from specific conversations rather than bulk extraction.
Unique: Builds a queryable index of Claude sessions without requiring full deserialization of each session file, using lazy-loading patterns to minimize memory footprint and enable fast searches across hundreds of conversations
vs alternatives: More efficient than generic file indexing tools because it understands Claude's session structure and can extract conversation-level metadata without parsing full file contents
Enables users to filter recovered files by type (code, documents, images), date range, or session ID before extraction, preventing bulk recovery of unwanted files and allowing targeted restoration of specific artifacts. Implements filtering logic at the extraction stage to avoid unnecessary deserialization and disk writes of irrelevant files.
Unique: Implements multi-dimensional filtering (type, date, session) at the extraction layer rather than post-hoc filtering, reducing I/O overhead and enabling users to avoid recovering files they don't need
vs alternatives: More granular than simple bulk recovery tools — allows users to recover specific subsets of artifacts without touching the entire session cache
Detects duplicate files across multiple sessions (using content hashing or filename matching) and handles naming conflicts when recovering multiple versions of the same file. Implements strategies like timestamp-based versioning or content-based deduplication to prevent overwriting files and preserve all recovered versions with clear naming.
Unique: Implements intelligent deduplication at recovery time rather than requiring manual cleanup afterward, using content hashing to identify true duplicates vs. files with the same name but different content
vs alternatives: Prevents data loss from overwriting files during recovery — generic file recovery tools often blindly overwrite or fail on conflicts, while this tool preserves all versions with clear naming
Exports recovered files to a user-specified output directory with configurable directory structure (flat, by session, by file type, or by date). Handles file permissions, creates necessary subdirectories, and provides progress reporting during batch recovery operations to manage large-scale artifact restoration.
Unique: Provides multiple output organization strategies (flat, by session, by type, by date) rather than forcing a single directory structure, allowing users to choose the layout that best fits their workflow
vs alternatives: More flexible than tools that dump all recovered files into a single directory — enables users to maintain logical organization and easily locate specific files after recovery
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 Claude-File-Recovery, recover files from your ~/.claude sessions at 33/100. Claude-File-Recovery, recover files from your ~/.claude sessions leads on adoption and 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
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