| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Exposes ESLint's linting engine as an MCP server, allowing remote clients (Claude, other LLM agents, or tools) to invoke ESLint rule checking and code analysis over the MCP protocol. The server wraps ESLint's core linting API and translates rule violations into structured MCP resource/tool responses, enabling stateless, request-response linting without direct filesystem access from the client.
Unique: Bridges ESLint (a mature, widely-adopted linting tool) into the MCP ecosystem, enabling AI agents and remote tools to invoke linting without direct filesystem access or subprocess spawning. Uses MCP's resource/tool abstraction to expose ESLint's linting API as a standardized remote service.
vs alternatives: Provides centralized, MCP-native linting for AI agents (vs. agents spawning ESLint subprocesses or calling ESLint via REST APIs), with full access to ESLint's rule ecosystem and configuration system.
Exposes ESLint rule definitions, descriptions, and documentation links as MCP resources or tools, allowing clients to query rule metadata without parsing ESLint's internal rule registry. The server introspects the loaded ESLint ruleset and surfaces rule names, descriptions, categories, and documentation URLs for use in AI-assisted code review or rule recommendation workflows.
Unique: Exposes ESLint's internal rule registry as queryable MCP resources, allowing clients to introspect rule definitions without parsing ESLint source code or documentation. Integrates with ESLint 9.x's flat config system to surface rule metadata dynamically.
vs alternatives: Provides programmatic access to rule metadata via MCP (vs. hardcoding rule descriptions or scraping ESLint docs), ensuring metadata stays in sync with the actual ESLint version running in the server.
Invokes ESLint's built-in auto-fix mechanism to automatically correct code violations where rules provide fix implementations. The server applies fixes to code strings or files, returns the corrected code, and optionally provides structured fix suggestions (before/after diffs, rule applied, confidence level) for client-side review or approval workflows.
Unique: Wraps ESLint's fix API in an MCP-accessible interface, allowing remote clients to request and apply fixes without spawning ESLint processes. Integrates with ESLint 9.x's rule fix system and provides structured fix metadata for client-side approval workflows.
vs alternatives: Enables AI agents to apply ESLint fixes as part of a larger workflow (vs. agents manually rewriting code or calling ESLint CLI), with full access to ESLint's fix implementations and the ability to preview fixes before applying them.
Accepts multiple code files or file paths in a single MCP request and returns aggregated linting results across all files. The server batches ESLint invocations, deduplicates configuration loading, and returns structured results grouped by file, enabling efficient bulk code analysis for large codebases or multi-file refactoring workflows.
Unique: Batches ESLint invocations to analyze multiple files in a single MCP request, reducing overhead vs. individual file requests. Aggregates results with file-level grouping and summary statistics for efficient bulk analysis.
vs alternatives: More efficient than making separate MCP requests per file (reduces network round-trips and server startup overhead), while providing structured aggregation suitable for dashboards or bulk refactoring workflows.
Automatically discovers and loads ESLint configuration files (.eslintrc.js, eslint.config.js, or package.json eslintConfig) from the server's working directory and validates the configuration for syntax errors or invalid rule options. The server exposes the loaded configuration as MCP resources, allowing clients to query which rules are enabled, their severity levels, and any configuration errors.
Unique: Exposes ESLint's configuration discovery and validation as MCP resources, allowing clients to introspect the active rule set without parsing config files manually. Integrates with ESLint 9.x's flat config system and legacy config support.
vs alternatives: Provides programmatic access to ESLint configuration via MCP (vs. clients parsing config files themselves or calling ESLint CLI with --print-config), ensuring config state is consistent with the server's linting behavior.
Supports linting of multiple languages (JavaScript, TypeScript, JSX, TSX) by leveraging ESLint's parser and plugin system. The server loads configured parsers (e.g., @typescript-eslint/parser) and plugins (e.g., @typescript-eslint/eslint-plugin) from the server environment, enabling language-specific rule checking and type-aware linting for TypeScript code.
Unique: Leverages ESLint 9.x's flat config system and plugin architecture to support multiple languages and type-aware linting. Integrates with @typescript-eslint and other language-specific plugins without requiring client-side parser installation.
vs alternatives: Provides type-aware linting for TypeScript via MCP (vs. clients running separate TypeScript linters or ESLint CLI with complex config), with full access to the @typescript-eslint rule ecosystem.
Converts natural language application descriptions into executable database schemas by parsing user intent through an LLM pipeline, inferring entity relationships, cardinality, and data types without manual schema definition. The system likely uses prompt engineering to constrain schema generation to Bubble's supported data model, then validates and materializes schemas in Bubble's backend database layer.
Unique: Integrates LLM-driven schema inference directly into Bubble's visual database builder, allowing non-technical users to generate normalized schemas through conversational prompts rather than manual table/field creation or SQL DDL statements
vs alternatives: Faster than traditional database design tools (Lucidchart, dbdiagram.io) for non-technical users because it eliminates the need to learn ER diagram syntax or database normalization rules
Translates natural language descriptions of application workflows (user actions, conditional logic, data transformations, multi-step processes) into executable Bubble workflows without requiring visual workflow builder expertise. The system maps user intent to Bubble's workflow primitives (actions, conditions, loops, API calls) through LLM-guided code generation, then validates and deploys workflows to Bubble's serverless execution layer.
Unique: Generates complete workflow definitions including conditional branching, loops, and API calls from natural language, mapping user intent to Bubble's visual workflow primitives without requiring users to interact with the workflow builder UI
vs alternatives: More accessible than Zapier or Make for complex multi-step workflows because it generates logic from natural language rather than requiring users to manually chain actions and configure conditions through a visual interface
Bubble AI scores higher at 71/100 vs @eslint/mcp at 35/100. @eslint/mcp leads on ecosystem, while Bubble AI is stronger on adoption and quality.
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Automatically generates data entry forms with built-in validation rules, error messages, and user feedback mechanisms inferred from the database schema and workflow requirements. The system maps schema field types and constraints to appropriate form inputs (text fields, dropdowns, date pickers, etc.), generates validation rules, and creates error handling workflows that provide users with clear feedback on submission failures.
Unique: Automatically generates form components with validation rules and error handling inferred from database schema constraints and workflow requirements, eliminating manual form configuration and validation logic implementation
vs alternatives: Simpler than manual form development in traditional frameworks because it automatically generates validation rules from schema constraints, whereas traditional development requires explicit validation configuration in form code
Automatically generates user authentication systems (signup, login, password reset) and role-based access control (RBAC) workflows based on natural language descriptions of user types and permissions. The system infers authentication requirements from application descriptions, generates secure authentication flows, and creates authorization rules that restrict access to features and data based on user roles.
Unique: Automatically generates complete authentication and authorization systems including signup, login, password reset, and role-based access control from natural language descriptions, eliminating manual implementation of security-critical authentication logic
vs alternatives: More secure than manual authentication implementation for non-technical users because it uses Bubble's built-in security features, whereas manual implementation is prone to security vulnerabilities (weak password hashing, SQL injection, etc.)
Automatically generates reports and data export functionality that allows users to export application data in standard formats (CSV, PDF, Excel) and view summarized data through generated dashboards and charts. The system infers reporting requirements from the application schema and workflows, generates report templates, and creates export workflows that transform application data into user-friendly formats.
Unique: Automatically generates reports, dashboards, and data export workflows from natural language descriptions, inferring aggregations and visualizations from application schema without requiring manual report design or data transformation logic
vs alternatives: Faster than manual report development in traditional BI tools (Tableau, Power BI) because it automatically generates reports from application data, whereas traditional BI tools require separate data modeling and report configuration
Enables multiple users to collaborate on application development through shared editing of generated applications, with real-time synchronization of changes and conflict resolution. The system maintains a shared application state that updates in real-time as team members make modifications through the visual editor or natural language prompts, allowing teams to build applications collaboratively without version control complexity.
Unique: Provides real-time collaborative editing of generated applications with automatic synchronization across team members, eliminating version control complexity and merge conflict management required in traditional development
vs alternatives: Simpler than traditional collaborative development (Git, GitHub) for non-technical teams because it provides real-time synchronization without version control concepts, whereas traditional development requires understanding branching, merging, and conflict resolution
Automatically generates responsive web UI components (pages, forms, tables, navigation, layouts) from natural language descriptions of application screens and user interactions. The system infers component hierarchy, styling, and responsive breakpoints through LLM analysis, then materializes components in Bubble's visual design system with built-in mobile responsiveness and accessibility features.
Unique: Generates complete responsive UI layouts from natural language by inferring component hierarchy, spacing, and breakpoints, then materializes them in Bubble's visual design system with automatic mobile responsiveness rather than requiring manual component placement and styling
vs alternatives: Faster than traditional UI design tools (Figma, Adobe XD) for non-technical users because it eliminates design tool learning curve and automatically handles responsive breakpoints, whereas design tools require manual layout work for each breakpoint
Orchestrates end-to-end application generation by coordinating database schema creation, workflow generation, and UI component generation from a single natural language application description. The system decomposes user intent into sub-tasks (data modeling, business logic, interface design), executes each through specialized LLM pipelines, then integrates outputs into a cohesive, deployable application with pre-configured data bindings and workflow triggers.
Unique: Coordinates multi-stage LLM-driven generation (schema → workflows → UI) from a single prompt, automatically integrating outputs with data bindings and event triggers, eliminating the need for users to manually connect database to business logic to UI
vs alternatives: Dramatically faster than traditional full-stack development (weeks to months) because it generates database, backend logic, and frontend UI simultaneously from natural language, whereas traditional development requires sequential phases of design, implementation, and integration
+6 more capabilities