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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Builds and maintains an in-memory index of all symbols (functions, classes, variables, types) across a codebase using language-aware parsing. Enables fast O(1) lookup of symbol definitions and all references without scanning the entire filesystem on each query. Uses tree-sitter or language-specific AST parsers to extract symbols with precise location metadata (file, line, column).
Unique: Implements MCP-native symbol indexing with tree-sitter AST parsing for language-aware extraction, avoiding regex-based approximations. Designed specifically for AI agent integration rather than as a general IDE plugin, enabling agents to make surgical edits based on precise symbol locations.
vs alternatives: Faster and more accurate than grep-based symbol search for large codebases, and more agent-friendly than IDE-bound tools like VS Code's symbol search since it exposes structured data via MCP protocol.
Enables precise code edits across multiple files by accepting symbol-aware edit instructions (e.g., 'replace all calls to function X with Y'). Parses edit requests, resolves symbols to their exact locations using the indexed codebase, and applies transformations while preserving code structure and formatting. Uses AST-based rewriting to ensure edits are syntactically correct.
Unique: Combines symbol indexing with AST-based rewriting to perform semantically-aware edits across files without requiring full semantic analysis. Designed for MCP agents to execute complex refactorings in a single operation rather than iterative file-by-file edits.
vs alternatives: More precise than language server-based refactoring tools because it operates on indexed symbol metadata, and faster than agent-driven iterative edits because it batches multi-file changes into single operations.
Provides fast file discovery across a codebase using glob patterns, regex filters, and language-based filtering (e.g., 'all Python files', 'all test files'). Implements efficient filesystem traversal with caching to avoid redundant scans. Returns file metadata (path, size, language, last modified) for downstream processing by agents.
Unique: Implements MCP-native file discovery with language detection and metadata caching, avoiding the need for agents to spawn shell commands or parse ls/find output. Integrates tightly with symbol indexing to enable filtered indexing (e.g., 'index only TypeScript files').
vs alternatives: Faster and more reliable than agent-driven shell command execution, and more flexible than IDE file pickers because it exposes raw file lists and metadata for programmatic filtering.
Extracts code snippets from files with surrounding context (imports, class definitions, function signatures) to provide agents with complete, compilable code fragments. Uses AST parsing to identify logical code boundaries and includes necessary dependencies. Supports extracting by line range, symbol name, or semantic block (e.g., 'entire function including decorators').
Unique: Uses AST parsing to extract semantically-complete code blocks with automatic dependency resolution, rather than naive line-range extraction. Designed for AI agents to receive compilable, self-contained code snippets that can be analyzed or modified without additional context gathering.
vs alternatives: More intelligent than simple line-range extraction because it understands code structure and includes necessary imports/definitions. More efficient than agents manually gathering context because it resolves dependencies automatically.
Monitors the filesystem for changes (file creation, modification, deletion) and incrementally updates the symbol index without full re-indexing. Uses filesystem watchers (inotify on Linux, FSEvents on macOS, ReadDirectoryChangesW on Windows) to detect changes with minimal latency. Applies delta updates to the index to maintain consistency with the current codebase state.
Unique: Implements native filesystem watching with delta-based index updates, avoiding the need to re-parse the entire codebase on every change. Designed for long-running MCP sessions where agents make iterative modifications and need current symbol information.
vs alternatives: More efficient than full re-indexing on every change, and more responsive than polling-based approaches. Enables agents to work with current codebase state without manual index refresh commands.
Provides structured APIs for agents to navigate code relationships (callers, callees, type definitions, inheritance hierarchies) without parsing. Returns navigation results as structured JSON with file paths, line numbers, and symbol metadata. Supports traversing call graphs, finding implementations of interfaces, and discovering all usages of a symbol.
Unique: Exposes structured code navigation APIs designed specifically for AI agents, returning JSON-serializable call graphs and relationship data rather than requiring agents to parse IDE output or AST dumps. Integrates with symbol index to enable fast traversal without re-parsing.
vs alternatives: More agent-friendly than language server protocols because it returns structured data directly. More efficient than agents performing their own AST traversal because it leverages pre-indexed relationships.
Implements the Model Context Protocol (MCP) server specification, exposing all file and code operations as standardized MCP tools that agents can discover and invoke. Handles MCP request/response serialization, error handling, and capability advertisement. Enables seamless integration with MCP-compatible clients like Devin, Claude, and custom agent frameworks without custom integration code.
Unique: Implements MCP server specification natively, enabling direct integration with any MCP-compatible agent without custom adapters. Designed as a first-class MCP tool rather than a library or plugin, making it composable with other MCP servers in agent orchestration frameworks.
vs alternatives: More standardized and composable than custom REST APIs or agent-specific integrations. Enables agents to discover and use capabilities without hardcoded tool definitions.
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 Files at 24/100.
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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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