| Ecosystem |
| 0 |
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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes Gcore Cloud infrastructure APIs (compute, storage, networking) through the Model Context Protocol, enabling LLM agents and Claude to provision, configure, and manage cloud resources by translating natural language requests into authenticated API calls. Implements MCP server pattern with tool registration for resource CRUD operations, handling authentication via Gcore API keys and maintaining session state across multi-step provisioning workflows.
Unique: Official Gcore MCP server implementation providing native integration between Claude/LLM agents and Gcore Cloud APIs through standardized MCP protocol, eliminating need for custom API client wrappers and enabling declarative resource management via natural language
vs alternatives: Tighter integration than generic cloud SDKs because it's officially maintained by Gcore and optimized for MCP's tool-calling semantics, vs. building custom MCP wrappers around Gcore's REST API
Enables LLM agents to execute complex, multi-step infrastructure workflows (e.g., provision VM → configure networking → deploy application) by maintaining context across sequential tool calls and handling dependencies between resources. Uses MCP's request/response pattern to chain operations, with implicit state tracking through conversation history and explicit resource IDs returned from each step.
Unique: Leverages MCP's stateless tool-calling model combined with LLM's reasoning to implicitly orchestrate infrastructure workflows, where agent maintains logical flow and resource dependencies through conversation context rather than explicit workflow engine
vs alternatives: More flexible than declarative IaC tools (Terraform) for exploratory/interactive infrastructure setup, but less reliable than explicit orchestration engines (Kubernetes operators, Airflow) for production workflows due to lack of formal dependency DAGs
Provides read-only MCP tools to list, describe, and filter Gcore Cloud resources (VMs, storage buckets, networks, etc.) with structured JSON responses. Implements query patterns supporting filtering by tags, status, region, and other metadata, enabling agents to discover existing infrastructure and make decisions based on current cloud state without requiring manual API exploration.
Unique: Exposes Gcore's native resource filtering and listing APIs through MCP's tool interface, allowing agents to perform structured queries without learning Gcore's REST API pagination and filter syntax
vs alternatives: More discoverable than raw API documentation for LLM agents because tool schemas explicitly define available filters and response structure, vs. agents having to infer query patterns from API docs
Handles secure storage and injection of Gcore Cloud API credentials (API key and secret) into MCP tool calls, supporting multiple authentication patterns: environment variables, credential files, and runtime injection. Implements credential validation on server startup and per-request authentication header construction, ensuring all API calls are properly authenticated without exposing credentials in tool parameters.
Unique: Implements MCP-native credential handling pattern where secrets are managed by the server runtime rather than passed through tool parameters, preventing credential exposure in tool schemas or conversation logs
vs alternatives: More secure than passing credentials as tool parameters because they never appear in MCP protocol messages, vs. generic API client libraries that require explicit credential passing
Translates Gcore Cloud API errors (rate limits, validation failures, resource conflicts, timeouts) into structured MCP error responses with actionable guidance. Implements retry logic for transient failures (network timeouts, 5xx errors) and provides detailed error context (HTTP status, error codes, API messages) to enable agents to make recovery decisions or escalate to users.
Unique: Implements MCP-aware error handling that preserves Gcore API error semantics while translating them into tool-call failures that agents can reason about, with built-in retry logic for transient failures
vs alternatives: More intelligent than raw API error propagation because it distinguishes transient vs. permanent failures and implements automatic retries, vs. agents having to manually parse HTTP status codes and implement retry logic
Validates resource configuration parameters against Gcore Cloud's API schemas before submitting requests, catching invalid configurations early and providing detailed validation error messages. Implements schema definitions for each resource type (VM, storage, network) with constraints (required fields, valid enums, min/max values), enabling agents to understand valid configurations and users to get immediate feedback on misconfiguration.
Unique: Embeds Gcore Cloud resource schemas in MCP tool definitions, enabling client-side validation and schema introspection before API calls, vs. discovering valid configurations through trial-and-error API calls
vs alternatives: Faster feedback loop than server-side validation because validation happens before network round-trip, and provides schema documentation that helps agents understand valid configuration space
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 Gcore Cloud 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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