Hostinger vs IntelliCode
Side-by-side comparison to help you choose.
| Feature | Hostinger | IntelliCode |
|---|---|---|
| Type | MCP Server | Extension |
| UnfragileRank | 24/100 | 39/100 |
| Adoption | 0 | 1 |
| Quality | 0 | 0 |
| Ecosystem | 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 9 decomposed | 7 decomposed |
| Times Matched | 0 | 0 |
Exposes Hostinger's domain management APIs through the Model Context Protocol, enabling Claude and other MCP-compatible clients to programmatically register, transfer, renew, and configure DNS records for domains. Implements MCP server transport layer that translates domain operation requests into authenticated REST calls to Hostinger's backend, handling OAuth2 token management and request/response serialization.
Unique: Official Hostinger MCP server implementation that bridges Hostinger's REST API directly into the MCP protocol, enabling Claude and other MCP clients to manage domains as first-class agent capabilities without custom integration code. Uses Hostinger's native OAuth2 authentication flow within MCP's request/response model.
vs alternatives: Native Hostinger integration vs generic REST API wrappers — eliminates authentication boilerplate and provides domain-specific operation semantics (register, transfer, renew) as discrete MCP tools rather than raw HTTP methods
Enables creation, configuration, and management of Hostinger hosting services (shared hosting, VPS, cloud hosting) through MCP tools. Translates high-level provisioning intents (e.g., 'create a WordPress hosting plan') into Hostinger API calls that handle plan selection, resource allocation, billing setup, and service activation. Manages service state transitions (pending → active → suspended) and configuration updates.
Unique: Wraps Hostinger's hosting service provisioning APIs as MCP tools with semantic understanding of hosting plan hierarchies and service states, allowing agents to reason about resource allocation and service configurations at a higher abstraction level than raw API calls.
vs alternatives: Hostinger-native vs generic cloud provisioning tools — provides direct access to Hostinger's specific hosting plans and configurations without abstraction layers, reducing latency and enabling Hostinger-specific optimizations
Provides granular DNS record CRUD operations (A, AAAA, CNAME, MX, TXT, SRV records) and nameserver configuration through MCP tools. Implements validation of DNS record syntax, TTL management, and atomic batch operations for multi-record updates. Handles DNS propagation status checking and integrates with Hostinger's DNS infrastructure for real-time record updates.
Unique: Exposes Hostinger's DNS API through MCP with record-level granularity and batch operation support, enabling agents to perform complex DNS configurations (multi-record updates, nameserver changes) as atomic operations rather than sequential individual calls.
vs alternatives: Hostinger-native DNS tools vs generic DNS providers — direct integration with Hostinger's DNS infrastructure eliminates provider abstraction and enables faster propagation through Hostinger's nameserver network
Manages email accounts, mailboxes, and email forwarding rules through MCP tools. Enables creation of email accounts with quota management, configuration of email forwarding, spam filtering, and autoresponders. Integrates with Hostinger's email infrastructure to handle IMAP/SMTP configuration and mailbox provisioning. Supports bulk email account operations for multi-user domains.
Unique: Integrates Hostinger's email infrastructure management into MCP with support for bulk operations and quota management, allowing agents to provision complete email environments (accounts, forwarding, filtering) as coordinated operations rather than individual API calls.
vs alternatives: Hostinger-native email tools vs generic email providers — direct integration with Hostinger's email infrastructure provides faster provisioning and eliminates need for separate email provider integration
Manages SSL/TLS certificates (Let's Encrypt, premium certificates) and HTTPS configuration through MCP tools. Handles certificate issuance, renewal, installation, and domain validation workflows. Supports automatic certificate renewal scheduling and provides certificate status monitoring. Integrates with Hostinger's certificate provisioning system for seamless HTTPS enablement.
Unique: Wraps Hostinger's certificate provisioning system as MCP tools with integrated domain validation handling and automatic renewal scheduling, enabling agents to manage complete HTTPS lifecycle (provisioning, validation, renewal, enforcement) without manual intervention.
vs alternatives: Hostinger-native SSL tools vs standalone certificate providers — integrated with Hostinger's hosting infrastructure for faster provisioning and automatic certificate installation without separate provider management
Manages website backups and restore operations through MCP tools. Enables scheduling automated backups, creating on-demand backups, listing backup history, and restoring websites to previous states. Supports granular restore options (full site, database only, files only) and backup retention policies. Integrates with Hostinger's backup storage infrastructure for reliable backup management.
Unique: Exposes Hostinger's backup infrastructure through MCP with granular restore options and automated scheduling, enabling agents to manage complete backup lifecycle (scheduling, creation, retention, restore) as coordinated operations with async status tracking.
vs alternatives: Hostinger-native backup tools vs third-party backup providers — integrated with Hostinger's hosting infrastructure for faster backup creation and restore without data transfer overhead
Provides access to website analytics and performance metrics through MCP tools. Exposes visitor statistics, traffic sources, page performance data, and server resource utilization. Integrates with Hostinger's analytics infrastructure to provide real-time and historical performance data. Supports custom date ranges and metric filtering for targeted analysis.
Unique: Integrates Hostinger's analytics infrastructure into MCP with support for custom date ranges and metric filtering, enabling agents to query and analyze website performance data without external analytics platform integration.
vs alternatives: Hostinger-native analytics vs third-party analytics platforms — direct access to hosting-level metrics (server resources, bandwidth) without requiring separate analytics tool integration
Manages application and CMS deployments (WordPress, Drupal, Joomla, custom apps) through MCP tools. Enables one-click installation, configuration, updates, and plugin/theme management. Supports automated deployment workflows with environment configuration and database setup. Integrates with Hostinger's application marketplace and deployment infrastructure.
Unique: Wraps Hostinger's application marketplace and deployment infrastructure as MCP tools with integrated configuration management, enabling agents to provision and manage complete CMS/application environments (installation, configuration, updates) as coordinated operations.
vs alternatives: Hostinger-native deployment tools vs manual CMS setup — automated installation and configuration without manual dashboard navigation, with integrated plugin/theme management
+1 more capabilities
Provides IntelliSense completions ranked by a machine learning model trained on patterns from thousands of open-source repositories. The model learns which completions are most contextually relevant based on code patterns, variable names, and surrounding context, surfacing the most probable next token with a star indicator in the VS Code completion menu. This differs from simple frequency-based ranking by incorporating semantic understanding of code context.
Unique: Uses a neural model trained on open-source repository patterns to rank completions by likelihood rather than simple frequency or alphabetical ordering; the star indicator explicitly surfaces the top recommendation, making it discoverable without scrolling
vs alternatives: Faster than Copilot for single-token completions because it leverages lightweight ranking rather than full generative inference, and more transparent than generic IntelliSense because starred recommendations are explicitly marked
Ingests and learns from patterns across thousands of open-source repositories across Python, TypeScript, JavaScript, and Java to build a statistical model of common code patterns, API usage, and naming conventions. This model is baked into the extension and used to contextualize all completion suggestions. The learning happens offline during model training; the extension itself consumes the pre-trained model without further learning from user code.
Unique: Explicitly trained on thousands of public repositories to extract statistical patterns of idiomatic code; this training is transparent (Microsoft publishes which repos are included) and the model is frozen at extension release time, ensuring reproducibility and auditability
vs alternatives: More transparent than proprietary models because training data sources are disclosed; more focused on pattern matching than Copilot, which generates novel code, making it lighter-weight and faster for completion ranking
IntelliCode scores higher at 39/100 vs Hostinger at 24/100. Hostinger leads on ecosystem, while IntelliCode is stronger on adoption and quality.
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Analyzes the immediate code context (variable names, function signatures, imported modules, class scope) to rank completions contextually rather than globally. The model considers what symbols are in scope, what types are expected, and what the surrounding code is doing to adjust the ranking of suggestions. This is implemented by passing a window of surrounding code (typically 50-200 tokens) to the inference model along with the completion request.
Unique: Incorporates local code context (variable names, types, scope) into the ranking model rather than treating each completion request in isolation; this is done by passing a fixed-size context window to the neural model, enabling scope-aware ranking without full semantic analysis
vs alternatives: More accurate than frequency-based ranking because it considers what's in scope; lighter-weight than full type inference because it uses syntactic context and learned patterns rather than building a complete type graph
Integrates ranked completions directly into VS Code's native IntelliSense menu by adding a star (★) indicator next to the top-ranked suggestion. This is implemented as a custom completion item provider that hooks into VS Code's CompletionItemProvider API, allowing IntelliCode to inject its ranked suggestions alongside built-in language server completions. The star is a visual affordance that makes the recommendation discoverable without requiring the user to change their completion workflow.
Unique: Uses VS Code's CompletionItemProvider API to inject ranked suggestions directly into the native IntelliSense menu with a star indicator, avoiding the need for a separate UI panel or modal and keeping the completion workflow unchanged
vs alternatives: More seamless than Copilot's separate suggestion panel because it integrates into the existing IntelliSense menu; more discoverable than silent ranking because the star makes the recommendation explicit
Maintains separate, language-specific neural models trained on repositories in each supported language (Python, TypeScript, JavaScript, Java). Each model is optimized for the syntax, idioms, and common patterns of its language. The extension detects the file language and routes completion requests to the appropriate model. This allows for more accurate recommendations than a single multi-language model because each model learns language-specific patterns.
Unique: Trains and deploys separate neural models per language rather than a single multi-language model, allowing each model to specialize in language-specific syntax, idioms, and conventions; this is more complex to maintain but produces more accurate recommendations than a generalist approach
vs alternatives: More accurate than single-model approaches like Copilot's base model because each language model is optimized for its domain; more maintainable than rule-based systems because patterns are learned rather than hand-coded
Executes the completion ranking model on Microsoft's servers rather than locally on the user's machine. When a completion request is triggered, the extension sends the code context and cursor position to Microsoft's inference service, which runs the model and returns ranked suggestions. This approach allows for larger, more sophisticated models than would be practical to ship with the extension, and enables model updates without requiring users to download new extension versions.
Unique: Offloads model inference to Microsoft's cloud infrastructure rather than running locally, enabling larger models and automatic updates but requiring internet connectivity and accepting privacy tradeoffs of sending code context to external servers
vs alternatives: More sophisticated models than local approaches because server-side inference can use larger, slower models; more convenient than self-hosted solutions because no infrastructure setup is required, but less private than local-only alternatives
Learns and recommends common API and library usage patterns from open-source repositories. When a developer starts typing a method call or API usage, the model ranks suggestions based on how that API is typically used in the training data. For example, if a developer types `requests.get(`, the model will rank common parameters like `url=` and `timeout=` based on frequency in the training corpus. This is implemented by training the model on API call sequences and parameter patterns extracted from the training repositories.
Unique: Extracts and learns API usage patterns (parameter names, method chains, common argument values) from open-source repositories, allowing the model to recommend not just what methods exist but how they are typically used in practice
vs alternatives: More practical than static documentation because it shows real-world usage patterns; more accurate than generic completion because it ranks by actual usage frequency in the training data