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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Converts addresses to geographic coordinates (latitude/longitude) and vice versa using Google Maps Geocoding API. Implements MCP tool protocol to expose geocoding operations as callable functions that LLM agents can invoke, with request/response marshaling handled by the MCP server abstraction layer. Supports batch geocoding through repeated tool invocations within a single agent session.
Unique: Exposes Google Maps geocoding as an MCP tool callable by LLM agents, allowing natural language location queries ('Where is the White House?') to be resolved to coordinates without requiring the agent to understand API schemas or authentication. The MCP abstraction handles protocol serialization, allowing the agent to treat geocoding as a first-class capability alongside reasoning.
vs alternatives: Unlike direct REST API calls, the MCP wrapper eliminates the need for agents to manage authentication, request formatting, and response parsing — the agent simply invokes a tool and receives structured results.
Computes optimal routes between two or more locations using Google Maps Directions API, returning turn-by-turn instructions, distance, duration, and alternative routes. Implements MCP tool interface that accepts origin/destination pairs and optional parameters (mode of transport, waypoints, avoid tolls/highways) and returns detailed route geometry and step-by-step navigation instructions.
Unique: Wraps Google Maps Directions API as an MCP tool, enabling LLM agents to reason about travel logistics without understanding routing algorithms or API mechanics. Agents can naturally express routing intent ('What's the fastest route from A to B avoiding tolls?') and receive structured route data suitable for further processing or presentation.
vs alternatives: Compared to raw API integration, the MCP abstraction allows agents to compose routing queries with other tools (e.g., place search, distance matrix) in a single reasoning loop without context switching or manual API orchestration.
Searches for places (businesses, landmarks, geographic features) by name, type, or proximity using Google Maps Places API. Implements MCP tool that accepts search queries and optional location bias, returning place details including name, address, phone, website, ratings, and opening hours. Supports both text search (free-form queries) and nearby search (places within radius of coordinates).
Unique: Exposes Google Places API as an MCP tool, allowing agents to discover and retrieve business information through natural language queries rather than structured API calls. The tool abstracts away pagination, result ranking, and place ID management, presenting search results as a simple list the agent can reason over.
vs alternatives: Unlike direct Places API usage, the MCP wrapper allows agents to combine place search with other location tools (geocoding, directions) in a single reasoning session, enabling workflows like 'Find Italian restaurants near my hotel and show me directions to the closest one.'
Retrieves comprehensive details for a specific place using its Google Maps Place ID, including full address, phone, website, hours, ratings, reviews, photos, and business attributes. Implements MCP tool that accepts a place ID (obtained from search results) and returns detailed place information. Handles authentication and API versioning internally, abstracting complexity from the agent.
Unique: Provides a dedicated MCP tool for detailed place information, allowing agents to perform two-phase discovery: first search for places, then retrieve full details for selected results. This separation enables efficient API usage and allows agents to reason about which places warrant detailed inspection.
vs alternatives: Compared to embedding all place details in search results, the dedicated details tool reduces API payload and allows agents to request only the information they need, improving latency and cost efficiency.
Computes distances and travel times between multiple origin-destination pairs in a single API call using Google Maps Distance Matrix API. Implements MCP tool that accepts arrays of origins and destinations, returning a matrix of distances and durations for each pair. Supports multiple travel modes (driving, walking, transit, bicycling) and optional traffic conditions.
Unique: Exposes Distance Matrix API as an MCP tool, enabling agents to compute bulk distance/duration calculations in a single operation rather than making individual direction requests. The tool returns structured matrix data that agents can analyze for optimization decisions without understanding matrix algebra or API mechanics.
vs alternatives: Compared to calling directions API repeatedly for each origin-destination pair, the distance matrix tool is significantly more efficient for multi-location problems, reducing API calls and latency by an order of magnitude.
Implements the Model Context Protocol (MCP) server abstraction that exposes all Google Maps capabilities as callable tools to LLM clients. Uses MCP's tool definition schema to declare available functions (geocoding, directions, place search, etc.) with input/output specifications, allowing clients to discover capabilities and invoke them with type-safe request/response handling. Manages authentication, error handling, and response marshaling transparently.
Unique: Implements the full MCP server pattern for Google Maps, including tool definition, request routing, authentication management, and response serialization. The server acts as a bridge between LLM agents and Google Maps APIs, translating high-level tool invocations into authenticated API calls and structured responses.
vs alternatives: Unlike direct API integration or custom REST wrappers, the MCP approach provides a standardized, discoverable interface that works with any MCP-compatible client (Claude, custom agents, etc.) without client-specific code.
Manages Google Maps API authentication by accepting an API key (via environment variable or configuration) and automatically including it in all outbound API requests. Implements credential handling patterns that prevent key exposure in logs or error messages, and validates key validity before tool invocation. Supports key rotation and configuration reloading without server restart.
Unique: Implements credential management at the MCP server level, ensuring API keys are never exposed to LLM agents or included in tool invocations. The server handles all authentication internally, presenting a credential-agnostic interface to clients.
vs alternatives: Compared to passing API keys as tool parameters or storing them in agent context, server-level credential management prevents accidental exposure and allows centralized key rotation without agent changes.
Implements error handling for Google Maps API failures (rate limiting, invalid requests, service unavailability) by catching API errors, translating them to MCP error responses, and providing actionable error messages to agents. Includes retry logic for transient failures (network timeouts, temporary service unavailability) and graceful degradation when optional features are unavailable (e.g., traffic data).
Unique: Implements error handling at the MCP server boundary, translating Google Maps API errors into MCP-compliant error responses that agents can understand and act upon. The server absorbs transient failures and retries automatically, reducing the burden on agents to handle low-level API issues.
vs alternatives: Compared to exposing raw API errors to agents, the MCP server's error abstraction provides consistent error semantics across all tools and enables centralized retry logic that benefits all location queries.
+1 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.
Supabase scores higher at 42/100 vs Google Maps at 22/100.
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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
+1 more capabilities