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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Parses human-readable time expressions (e.g., 'next Tuesday at 3pm', 'in 2 hours', 'last month') into structured datetime objects through an NLP-based interpretation layer. The MCP server accepts natural language input and converts it to standardized datetime representations, handling relative references, fuzzy matching, and colloquial expressions without requiring strict formatting.
Unique: Exposes natural language time parsing as an MCP tool, allowing any MCP-compatible client (Claude, custom agents) to invoke fuzzy datetime interpretation without embedding a separate NLP library or calling external APIs
vs alternatives: More flexible than rigid regex-based date parsing and more lightweight than calling a full LLM for every date interpretation, since the logic is encapsulated in a reusable MCP service
Converts datetime values between multiple standard formats (ISO 8601, Unix timestamp, RFC 2822, custom strftime patterns, human-readable strings) through a format-agnostic conversion engine. The MCP server accepts a datetime in one format and outputs it in any requested target format, handling edge cases like leap seconds and daylight saving time transitions.
Unique: Provides format conversion as a composable MCP tool rather than requiring clients to implement format parsing logic themselves, reducing boilerplate in agents and workflows that juggle multiple datetime standards
vs alternatives: More convenient than calling moment.js, dateutil, or chrono separately in each client, and avoids the overhead of embedding a full datetime library when only format conversion is needed
Converts datetime values between timezones using IANA timezone database (tzdata) and handles daylight saving time transitions automatically. The MCP server accepts a datetime with a source timezone and converts it to a target timezone, accounting for DST rules and historical timezone changes. Supports both named timezones (e.g., 'America/New_York') and UTC offsets.
Unique: Encapsulates timezone conversion logic as an MCP tool, allowing LLM agents to reason about timezones without embedding timezone libraries or making external API calls, with automatic DST handling built-in
vs alternatives: More reliable than manual UTC offset calculations and more accessible to non-backend developers building LLM agents, compared to requiring direct use of libraries like pytz or moment-timezone
Calculates time differences between two datetimes and formats them as human-readable relative expressions (e.g., '2 hours ago', 'in 3 days', 'last month'). The MCP server computes the delta and applies intelligent rounding and pluralization rules to generate natural language output suitable for UI display or conversational contexts.
Unique: Provides relative time formatting as an MCP tool, enabling LLM agents to generate natural language time expressions without embedding a separate formatting library or hardcoding pluralization rules
vs alternatives: More flexible than static templates and more consistent than having each client implement relative time formatting independently, reducing duplication across distributed agent systems
Retrieves the current system time and date in multiple formats and timezones through a simple query endpoint. The MCP server returns the current moment as an ISO 8601 string, Unix timestamp, and human-readable format, optionally adjusted to a specified timezone. Useful for agents that need to anchor relative time calculations or verify the current moment.
Unique: Exposes current time as an MCP resource, allowing agents to query the canonical server time without implementing their own clock or timezone logic, with multi-format output for flexibility
vs alternatives: More reliable than agents using their local system time (which may be out of sync) and simpler than agents making HTTP calls to time APIs, since the time service is co-located with the MCP server
Parses human-readable duration expressions (e.g., '2 hours 30 minutes', '1 week', '45 days') into structured duration objects and performs arithmetic operations (addition, subtraction, comparison). The MCP server accepts natural language or ISO 8601 duration format and converts to total seconds, milliseconds, or human-readable breakdown.
Unique: Provides duration parsing as an MCP tool, allowing agents to interpret user-specified time intervals without embedding a separate duration parser, and supporting both natural language and ISO 8601 formats
vs alternatives: More flexible than regex-based duration parsing and more accessible than requiring agents to implement ISO 8601 duration parsing themselves, with support for colloquial expressions like 'a couple hours'
Provides a queryable list of valid IANA timezone identifiers and validates whether a given timezone name is recognized by the system. The MCP server returns all supported timezones (e.g., 'America/New_York', 'Europe/London') and can validate user input against this list, useful for autocomplete and error handling in timezone selection UIs.
Unique: Exposes the system's timezone database as an MCP resource, allowing agents and UIs to discover and validate timezones without embedding or maintaining a separate timezone list
vs alternatives: More reliable than hardcoded timezone lists and more efficient than agents querying external timezone APIs, since the data is served locally by the MCP server
Processes multiple datetime values in a single MCP call, applying the same operation (conversion, formatting, timezone adjustment) to a batch of inputs. The server accepts an array of datetimes and a transformation specification, returning an array of transformed results, useful for bulk operations in data pipelines.
Unique: Supports batch datetime operations through a single MCP call, reducing round-trip overhead compared to processing items individually, and enabling efficient bulk transformations in data pipelines
vs alternatives: More efficient than looping through individual conversion calls and more convenient than implementing batch logic in client code, especially for agents orchestrating multi-step workflows
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 Time at 25/100.
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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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