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| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 8 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes the current date and time with full timezone support through the MCP protocol, returning both ISO 8601 timestamps and human-readable formats. Implements timezone-aware datetime calculations using Python's pytz library integrated into the FastMCP framework, allowing LLMs to query the server for the precise current moment in any specified timezone without relying on training data cutoffs or hallucinated timestamps.
Unique: Designed specifically for LLM temporal reasoning rather than general-purpose time APIs — returns both machine-readable ISO 8601 and human-contextual information (e.g., business hours, weekend status) in a single call, addressing the architectural gap where LLMs lack real-time temporal grounding
vs alternatives: Unlike generic system time APIs or web services, this tool is optimized for LLM consumption with human-contextual metadata built-in, eliminating the need for LLMs to perform secondary reasoning about what the current time means
Converts arbitrary timestamp formats (Unix epoch, ISO 8601, RFC 2822, human-readable strings) into normalized datetime objects with timezone awareness. Implements a format-detection pipeline using Python's dateutil.parser combined with regex-based heuristics to identify and parse ambiguous timestamp strings, exposing the parsed result through MCP with validation and error reporting for malformed inputs.
Unique: Combines dateutil's fuzzy parsing with format-detection heuristics to handle the ambiguity that LLMs encounter when processing real-world temporal data, returning both the parsed result and metadata about which format was detected — enabling LLMs to reason about timestamp reliability
vs alternatives: More flexible than strict format validators and more reliable than LLM-native parsing, which frequently hallucinates timestamps; provides confidence scores and format detection that help LLMs understand parsing uncertainty
Calculates the elapsed time between two timestamps or from a timestamp to the present, returning durations in multiple human-readable formats (days, hours, minutes, seconds, and natural language descriptions). Implements timezone-aware datetime subtraction using Python's datetime module with support for DST transitions, exposing results through MCP with both machine-readable duration objects and human-contextual descriptions like 'about 2 weeks' or 'less than a minute'.
Unique: Specifically designed for LLM temporal reasoning by returning both precise numerical durations and human-contextual descriptions in a single call, eliminating the need for LLMs to perform secondary formatting or interpretation of raw time differences
vs alternatives: Unlike generic time libraries that return raw seconds or timedelta objects, this tool provides LLM-optimized output with natural language descriptions and relative time phrases that LLMs can directly use in responses without additional processing
Adds or subtracts time intervals (days, hours, minutes, seconds) from a given timestamp, returning the resulting datetime with full timezone awareness and DST handling. Implements interval arithmetic using Python's timedelta objects combined with pytz timezone handling, allowing LLMs to perform forward and backward temporal projections for scheduling, deadline calculation, and temporal reasoning without manual arithmetic.
Unique: Provides timezone-aware interval arithmetic specifically for LLM use cases, handling DST transitions automatically and returning both the computed datetime and human-readable format in a single call — eliminating the need for LLMs to reason about timezone edge cases
vs alternatives: More reliable than LLM-native date arithmetic (which frequently produces off-by-one errors) and more LLM-friendly than raw timedelta objects, with automatic DST handling that generic time libraries require manual configuration for
Analyzes a timestamp and returns contextual information about when that moment falls in human terms: whether it's a weekday or weekend, business hours or after-hours, morning/afternoon/evening, and other human-centric temporal categories. Implements context detection using configurable business hour definitions and calendar logic, exposing results through MCP as structured metadata that helps LLMs reason about temporal significance beyond raw timestamps.
Unique: Designed from collaborative human-AI development to provide the specific contextual dimensions that LLMs need for temporal reasoning — business hours, weekday/weekend, time of day — rather than raw timestamp data, addressing the architectural gap where LLMs lack intuitive understanding of temporal significance
vs alternatives: Unlike generic datetime libraries that return only raw date/time components, this tool provides LLM-optimized contextual metadata that enables more human-aware temporal reasoning without requiring LLMs to implement business logic themselves
Converts raw duration values (seconds, milliseconds, or timedelta objects) into multiple human-readable formats: natural language descriptions ('about 2 weeks'), abbreviated formats ('2w 3d'), and detailed breakdowns (days/hours/minutes/seconds). Implements format selection logic that chooses the most appropriate representation based on duration magnitude, exposing results through MCP with both machine-readable and human-contextual outputs for LLM consumption.
Unique: Provides LLM-optimized duration formatting that returns multiple representation styles in a single call, allowing LLMs to choose the most appropriate format for their output context without requiring secondary formatting logic
vs alternatives: More flexible than fixed-format duration libraries and more LLM-friendly than raw timedelta objects, with automatic format selection that adapts to duration magnitude and context
Registers all temporal tools as callable MCP endpoints through the FastMCP framework, managing tool schema definition, input validation, and protocol-level communication with MCP clients. Implements a single global FastMCP instance that handles tool discovery, parameter marshalling, and response serialization, enabling seamless integration with Claude and other LLM applications that support the Model Context Protocol without requiring manual API configuration.
Unique: Leverages FastMCP's declarative tool registration pattern to expose temporal capabilities as first-class MCP tools with automatic schema generation and protocol handling, eliminating manual API configuration and enabling direct LLM integration without middleware
vs alternatives: Simpler and more maintainable than custom MCP server implementations, with automatic schema generation and protocol compliance built-in; more direct than REST API wrappers, with lower latency and tighter LLM integration
Manages timezone information using the pytz library with automatic Daylight Saving Time (DST) transition handling across all temporal calculations. Implements timezone-aware datetime arithmetic that accounts for DST boundaries, ensuring that operations like adding days or calculating durations across DST transitions produce correct results without manual offset adjustments. Exposes timezone validation and DST status information through MCP for LLM awareness of temporal edge cases.
Unique: Provides LLM-aware DST handling that automatically accounts for timezone transitions in all temporal calculations, eliminating the need for LLMs to manually reason about offset changes or DST edge cases — a common source of temporal errors in LLM-generated code
vs alternatives: More reliable than LLM-native timezone arithmetic (which frequently produces off-by-one-hour errors across DST boundaries) and more transparent than opaque timezone libraries, with explicit DST status information that helps LLMs understand temporal uncertainty
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 passage-of-time-mcp at 30/100.
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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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