| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Creates time tracking entries in Toggl by translating MCP tool calls into Toggl API REST requests. Implements the Model Context Protocol as a server that exposes time entry creation as a callable tool, allowing LLM agents and Claude instances to initiate time tracking without direct API knowledge. Handles authentication via Toggl API token and marshals user intent (task description, duration, project/tag metadata) into properly formatted Toggl API payloads.
Unique: Exposes Toggl time tracking as a native MCP tool callable by Claude, eliminating the need for custom integrations or API wrappers — the MCP server acts as a thin adapter layer that translates Claude's tool invocations directly into Toggl REST API calls with minimal abstraction
vs alternatives: Simpler than building custom Claude plugins or REST API wrappers because it leverages MCP's standardized tool-calling protocol, making it immediately compatible with any MCP-aware client without additional configuration
Manages Toggl API authentication by accepting and validating an API token, then injecting it into all outbound HTTP requests as a Basic Auth header (token as username, 'api_token' as password per Toggl's authentication scheme). Stores the token in environment variables or configuration at startup and applies it transparently to all subsequent API calls without requiring per-request token passing from the MCP client.
Unique: Centralizes Toggl authentication at the MCP server layer rather than requiring Claude or the client to handle credentials, using Toggl's standard Basic Auth scheme with token-as-username pattern — this keeps secrets out of LLM context and simplifies credential rotation
vs alternatives: More secure than passing API tokens through Claude's context because credentials never reach the LLM; simpler than OAuth flows because Toggl's API token model doesn't require token refresh or consent flows
Defines and exposes time-tracking operations as MCP-compliant tool schemas that Claude can discover and invoke. The server implements the MCP tools/list and tools/call endpoints, advertising available tools (e.g., 'create_time_entry') with JSON schema describing parameters (task name, duration, project, tags) and return types. Claude uses these schemas to understand what operations are available and automatically constructs valid tool calls without manual prompt engineering.
Unique: Implements MCP's standardized tool schema protocol, allowing Claude to discover and understand Toggl operations through JSON Schema rather than hardcoded prompts — this makes the integration self-documenting and compatible with any MCP-aware client without custom integration code
vs alternatives: More discoverable than REST API documentation because schemas are machine-readable and automatically exposed to Claude; more maintainable than prompt-based tool descriptions because schema changes are centralized in the server
Retrieves time entries from Toggl API based on query parameters (date range, project filter, tag filter) and returns structured data to Claude. The MCP server translates query parameters into Toggl API GET requests (e.g., /api/v9/me/time_entries with date filters), parses the JSON response, and formats it for LLM consumption. Enables Claude to inspect logged time, verify entries before creating new ones, or generate reports without manual Toggl UI navigation.
Unique: Exposes Toggl's time entry query API as an MCP tool, allowing Claude to read time-tracking data without leaving the conversation — queries are parameterized and translated to Toggl API calls, enabling context-aware decisions based on logged time
vs alternatives: More integrated than asking users to manually check Toggl because Claude can query and analyze time data in real-time; more flexible than static reports because Claude can dynamically filter and interpret results
Fetches available projects and tags from the user's Toggl workspace via the Toggl API and exposes them as queryable data. The MCP server calls Toggl's /api/v9/me/projects and /api/v9/me/tags endpoints, caches the results, and provides them to Claude so it can reference valid project IDs and tag names when creating time entries. Prevents invalid project/tag references by allowing Claude to validate against the authoritative list.
Unique: Provides Claude with a queryable index of the user's Toggl workspace structure (projects and tags), enabling context-aware time entry creation without hardcoding or manual specification — acts as a knowledge base for valid references
vs alternatives: More intelligent than generic time tracking because Claude understands the user's specific project taxonomy; more reliable than free-form project names because it enforces valid IDs from the authoritative Toggl workspace
Implements the MCP server lifecycle using stdio-based transport, where the server reads MCP protocol messages from stdin and writes responses to stdout. Handles server initialization (capabilities negotiation), tool discovery, and tool invocation through the MCP protocol's request/response model. Runs as a long-lived process that Claude Desktop or another MCP client spawns and communicates with via standard input/output streams, eliminating the need for HTTP servers or port configuration.
Unique: Uses MCP's stdio transport protocol for server communication, avoiding HTTP/network complexity and enabling tight integration with Claude Desktop — the server is a simple stdin/stdout process that Claude spawns and manages directly
vs alternatives: Simpler than HTTP-based MCP servers because no port management or network configuration is needed; more secure than network-exposed servers because communication is local and process-isolated
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 Lazy Toggl MCP at 20/100.
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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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