| 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides bidirectional encoding/decoding across Base64, URL encoding, hex, and other standard formats through a unified MCP tool interface. Implements format-specific codec handlers that validate input and output types, enabling seamless conversion between text representations without manual string manipulation or external library dependencies.
Unique: Exposes encoding/decoding as MCP tools callable by LLM agents rather than requiring SDK imports, enabling agents to transparently handle format conversions as part of reasoning chains without context switching
vs alternatives: Simpler than building custom encoding logic in agent prompts or maintaining separate utility libraries, as it's directly callable via MCP function calling with type-safe schemas
Supports multiple hashing algorithms (MD5, SHA-1, SHA-256, SHA-512, and others) through a parameterized MCP tool that accepts input text and algorithm selection, returning hex-encoded hash digests. Implements algorithm validation to prevent unsupported hash function calls and provides consistent output formatting across all hash types.
Unique: Exposes hashing as an MCP tool with algorithm selection parameter, allowing agents to choose hash functions dynamically based on context rather than hardcoding a single algorithm
vs alternatives: More flexible than single-algorithm utilities because agents can select SHA-256 for security-critical paths and MD5 for legacy compatibility in the same workflow without code changes
Validates JSON data against a provided schema, returning detailed error messages for non-conforming data including field names, expected types, and constraint violations. Implements schema-based validation using standard JSON Schema format, enabling agents to validate API responses, configuration data, or user input without writing custom validation logic.
Unique: JSON Schema validation exposed as MCP tools with detailed error reporting, allowing agents to validate data conformance and generate actionable error messages without custom validation code
vs alternatives: More comprehensive than simple type checking because it validates against full JSON Schema including constraints, required fields, and nested structure requirements
Transforms CSV data into structured JSON arrays of objects (with header-based key mapping) and vice versa, handling delimiter detection, quote escaping, and header normalization. Implements row-by-row parsing that maps CSV columns to JSON object keys, preserving data types where possible and providing options for custom delimiters and quote characters.
Unique: Bidirectional conversion with configurable delimiters and header normalization, allowing agents to handle CSV variants (tab-separated, semicolon-delimited) without separate tool calls
vs alternatives: More flexible than fixed-format converters because it supports custom delimiters and quote handling, making it compatible with non-standard CSV exports from legacy systems
Converts JSON arrays of objects into Markdown table syntax with automatic column width calculation, header formatting, and alignment. Parses JSON structure to extract keys as table headers, iterates through objects to populate rows, and generates properly formatted Markdown with pipe delimiters and alignment indicators.
Unique: Generates Markdown tables directly from JSON with automatic header extraction and alignment, eliminating manual table construction in agent-generated documentation
vs alternatives: Faster than manually formatting tables in prompts because it handles alignment and escaping automatically, producing valid Markdown without trial-and-error
Parses HTML documents and extracts structured data into JSON format using CSS selectors or tag-based queries. Implements DOM traversal to identify elements, extract text content and attributes, and map them to JSON object structures with configurable key naming and nesting.
Unique: Provides CSS selector-based extraction from HTML with configurable JSON mapping, allowing agents to define extraction schemas without writing custom parsing code
vs alternatives: More flexible than regex-based HTML parsing because it understands DOM structure and can handle nested elements, making it robust against HTML formatting variations
Provides a regex testing tool that accepts a pattern string and input text, returning match results with captured groups, match positions, and validation feedback. Implements regex compilation with error handling, supports multiple regex flavors (JavaScript, Python-compatible), and returns structured results including all captured groups and match metadata.
Unique: Exposes regex testing as an MCP tool with structured match result output including all captured groups and positions, enabling agents to extract and validate text patterns without embedding regex logic in prompts
vs alternatives: Better than manual regex testing because it returns all captured groups and match metadata in structured format, making it easy for agents to use extracted data in subsequent steps
Analyzes text input to compute metrics including word count, character count, sentence count, average word length, readability scores, and keyword frequency distribution. Implements tokenization for word/sentence splitting, calculates linguistic metrics (Flesch-Kincaid grade level, etc.), and returns comprehensive statistics as structured JSON.
Unique: Computes multiple linguistic metrics (readability scores, keyword frequency, sentence structure) in a single tool call, providing agents with comprehensive text analysis without multiple tool invocations
vs alternatives: More comprehensive than simple word counting because it includes readability scores and keyword frequency, giving agents actionable insights about text quality and composition
+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.
Developer Utilities scores higher at 44/100 vs Supabase at 42/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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