| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $25/mo |
| Capabilities | 8 decomposed | 15 decomposed |
| Times Matched | 0 | 0 |
Exposes Next.js development server state and metadata through the Model Context Protocol (MCP) using stdio transport, enabling Claude and other MCP clients to query active routes, middleware configuration, build status, and server-side rendering details without direct filesystem access. Implements MCP resource and tool schemas that map to Next.js internal APIs, allowing structured queries about the running development environment.
Unique: Bridges Next.js development server internals directly into MCP protocol, allowing AI agents to query live app state without parsing source code or making HTTP requests to the dev server — uses stdio transport for zero-configuration local integration
vs alternatives: Unlike generic Next.js API clients or REST-based dev server inspection, this MCP server provides structured, schema-validated access to Next.js metadata through a standardized protocol that Claude and other AI tools natively understand
Scans the Next.js app directory structure and extracts metadata about all registered routes, including path patterns, dynamic segments, layouts, and page component locations. Implements directory traversal logic that understands Next.js file conventions (page.tsx, layout.tsx, route.ts) and maps them to runtime route definitions without requiring a full build or server restart.
Unique: Implements Next.js file convention parsing (page.tsx, layout.tsx, route.ts patterns) directly in the MCP server, enabling real-time route discovery without requiring a build step or accessing Next.js internal compiler state
vs alternatives: More accurate than regex-based route extraction because it understands Next.js file conventions; faster than building and inspecting the Next.js manifest because it reads the filesystem directly
Reads and parses the middleware.ts/middleware.js file from a Next.js project and exposes its configuration, matcher patterns, and execution order through MCP resources. Analyzes the middleware code structure to extract route matchers, conditional logic, and any custom headers or redirects defined, allowing AI agents to understand request processing pipelines without executing the middleware.
Unique: Parses Next.js middleware.ts as a static artifact and extracts matcher patterns and configuration without executing the middleware code, enabling safe inspection of request processing logic from within an AI agent context
vs alternatives: Safer and faster than running middleware in a test environment; more accurate than regex-based route matching because it understands Next.js matcher syntax natively
Monitors the Next.js development server's build state and exposes compilation errors, warnings, and build progress through MCP resources. Queries the dev server's internal build status (via internal APIs or log parsing) and surfaces TypeScript errors, module resolution failures, and other build-time diagnostics in a structured format that AI agents can parse and act upon.
Unique: Exposes Next.js dev server build state through MCP, allowing AI agents to query compilation status and errors without parsing console output or making direct HTTP requests to the dev server
vs alternatives: More reliable than parsing console logs because it accesses structured build state; more timely than waiting for CI/CD feedback because it reports live dev server status
Analyzes page and route component files to detect and expose rendering mode configuration (SSR, SSG, ISR, dynamic rendering) through static code analysis. Parses export statements for getServerSideProps, getStaticProps, getStaticPaths, and dynamic() calls, and identifies dynamic segments and searchParams usage to determine rendering behavior without executing the code.
Unique: Performs static code analysis on page components to infer rendering mode without executing the code, enabling AI agents to understand data fetching and rendering strategy for code generation and optimization
vs alternatives: More accurate than guessing based on file location because it reads actual export statements; faster than building and inspecting the Next.js manifest because it analyzes source code directly
Reads .env files, .env.local, and next.config.js from the Next.js project and exposes available environment variables and configuration options through MCP resources. Parses environment variable names and types (inferred from usage or explicit schema) and exposes Next.js configuration settings (image optimization, API routes, redirects, rewrites) in a structured format for AI agents to reference when generating code.
Unique: Exposes Next.js project configuration and environment variables through MCP, allowing AI agents to reference project-specific settings when generating code without requiring manual configuration input
vs alternatives: More reliable than hardcoding configuration assumptions because it reads actual project files; more complete than environment variable discovery alone because it also exposes next.config.js settings
Provides MCP tools that enable AI agents to generate or modify Next.js files with automatic path resolution, import statement generation, and file location validation. Understands Next.js file conventions and directory structure to suggest appropriate file locations for new pages, components, API routes, and middleware, and validates that generated imports will resolve correctly within the project structure.
Unique: Integrates Next.js file convention understanding directly into MCP tools, enabling AI agents to generate files in correct locations and with proper import paths without manual path specification
vs alternatives: More accurate than generic file generation because it understands Next.js-specific conventions; more reliable than AI-generated paths because it validates against actual project structure
Scans the Next.js project for reusable components, utilities, and hooks, and exposes their signatures, prop types, and usage patterns through MCP resources. Performs static analysis on component files to extract TypeScript/JSDoc type information, identifies commonly-used utilities, and tracks which components are used where, enabling AI agents to reference existing code when generating new features.
Unique: Performs static analysis on Next.js components to extract type information and usage patterns, enabling AI agents to discover and reuse existing components without manual documentation or imports
vs alternatives: More accurate than searching for components by name because it analyzes actual type signatures; more complete than component documentation because it discovers components automatically
Automatically inspects tabular data sources (Google Sheets, Airtable, Excel, CSV, SQL databases) to extract column names, infer field types (text, number, date, checkbox, etc.), and create bidirectional data bindings between UI components and source columns. Uses declarative component-to-column mappings that persist schema changes in real-time, enabling components to automatically reflect upstream data structure modifications without manual rebinding.
Unique: Glide's approach combines automatic schema introspection with declarative component binding, eliminating manual field mapping that competitors like Airtable require. The bidirectional sync model means changes to source column structure automatically propagate to UI components without developer intervention, reducing maintenance overhead for non-technical users.
vs alternatives: Faster to initial app than Airtable (which requires manual field configuration) and more flexible than rigid form builders because it adapts to evolving data structures automatically.
Provides 40+ pre-built, data-aware UI components (forms, tables, calendars, charts, buttons, text inputs, dropdowns, file uploads, maps, etc.) that automatically render responsively across mobile and desktop viewports. Components use a declarative binding syntax to connect to spreadsheet columns, with built-in support for computed fields, conditional visibility, and user-specific data filtering. Layout engine uses CSS Grid/Flexbox under the hood to adapt component sizing and positioning based on screen size without requiring manual breakpoint configuration.
Unique: Glide's component library is tightly integrated with data binding — components are not generic UI elements but data-aware objects that automatically sync with spreadsheet columns. This eliminates the disconnect between UI and data that exists in traditional form builders, where developers must manually wire component values to data sources.
vs alternatives: Faster to build than Bubble (which requires manual component-to-data wiring) and more mobile-optimized than Airtable's grid-centric interface, which prioritizes desktop spreadsheet metaphors over mobile-first design.
Glide scores higher at 70/100 vs next-devtools-mcp at 37/100. next-devtools-mcp leads on ecosystem, while Glide is stronger on adoption and quality.
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Enables multiple team members to edit apps simultaneously with role-based access control. Supports predefined roles (Owner, Editor, Viewer) with different permission levels: Owners can manage team members and publish apps, Editors can modify app design and data, Viewers can only view published apps. Team member limits vary by plan (2 free, 10 business, custom enterprise). Real-time collaboration on app design is not mentioned, suggesting changes may not be synchronized in real-time between editors.
Unique: Glide's team collaboration is built into the platform, meaning team members don't need separate accounts or complex permission configuration — they're invited via email and assigned roles directly in the app. This is more seamless than tools requiring external identity management.
vs alternatives: More integrated than Airtable (which requires separate workspace management) and simpler than GitHub-based collaboration (which requires version control knowledge), though less sophisticated than enterprise platforms with audit logging and approval workflows.
Provides pre-built app templates for common use cases (inventory management, CRM, project management, expense tracking, etc.) that users can clone and customize. Templates include sample data, pre-configured components, and example workflows, reducing time-to-first-app from hours to minutes. Templates are fully editable, allowing users to modify data sources, components, and workflows to match their specific needs. Template library is curated by Glide and updated regularly with new templates.
Unique: Glide's templates are fully functional apps with sample data and workflows, not just empty scaffolds. This allows users to immediately see how components work together and understand app structure before customizing, reducing the learning curve significantly.
vs alternatives: More complete than Airtable's templates (which are mostly empty bases) and more accessible than building from scratch, though less flexible than code-based frameworks where templates can be parameterized and generated programmatically.
Allows workflows to be triggered on a schedule (daily, weekly, monthly, or custom intervals) without manual intervention. Scheduled workflows execute at specified times and can perform batch operations (process pending records, send daily reports, sync data, etc.). Execution time is in UTC, and the exact scheduling mechanism (cron, quartz, custom) is undocumented. Failed scheduled tasks may or may not retry automatically (retry logic undocumented).
Unique: Glide's scheduled workflows are integrated with the workflow engine, meaning scheduled tasks can execute the same complex logic as event-triggered workflows (conditional logic, multi-step actions, API calls). This is more powerful than simple scheduled email tools because scheduled tasks can perform data transformations and cross-system synchronization.
vs alternatives: More integrated than Zapier's schedule trigger (which is limited to simple actions) and more accessible than cron jobs (which require server access and scripting knowledge), though less transparent about execution guarantees and failure handling than enterprise job schedulers.
Offers Glide Tables, a proprietary managed database alternative to external spreadsheets or databases, with automatic scaling and optimization for Glide apps. Glide Tables are stored in Glide's infrastructure and optimized for the data binding and query patterns used by Glide apps. Scaling limits are plan-dependent (25k-100k rows), with separate 'Big Tables' tier for larger datasets (exact scaling limits undocumented). Automatic backups and disaster recovery are mentioned but details are undocumented.
Unique: Glide Tables are optimized specifically for Glide's data binding and query patterns, meaning they're tightly integrated with the app builder and don't require separate database administration. This is more seamless than connecting external databases (which require schema design and optimization knowledge) but less flexible because data is locked into Glide's proprietary format.
vs alternatives: More managed than self-hosted databases (no administration required) and more integrated than external databases (no separate configuration), though less portable than standard databases because data cannot be easily exported or migrated.
Provides basic chart components (bar, line, pie, area charts) that visualize data from connected sources. Charts are configured visually by selecting data columns for axes, values, and grouping. Charts are responsive and adapt to mobile/tablet/desktop. Real-time updates are supported; charts refresh when underlying data changes. No custom chart types or advanced visualization options (3D, animations, etc.) are available.
Unique: Provides basic chart components with automatic real-time updates and responsive design, suitable for simple dashboards — most visual builders (Bubble, FlutterFlow) require chart plugins or custom code
vs alternatives: More integrated than Airtable's chart view because real-time updates are automatic; weaker than BI tools (Tableau, Looker) because no drill-down, filtering, or advanced visualization options
Allows users to query data using natural language (e.g., 'Show me all orders from last month with revenue > $5k') which is converted to structured database queries without SQL knowledge. Also includes AI-powered data extraction from unstructured text (emails, documents, images) to populate spreadsheet columns. Implementation details (LLM model, context window, fine-tuning approach) are undocumented, but the feature appears to use prompt-based query generation with fallback to manual query building if AI fails.
Unique: Glide's natural language query feature bridges the gap between spreadsheet users (who think in English) and database queries (which require SQL). Rather than teaching users SQL, it translates natural language to structured queries, lowering the barrier to data exploration. The data extraction capability extends this to unstructured sources, automating data entry from emails and documents.
vs alternatives: More accessible than Airtable's formula language or traditional SQL, and more integrated than bolt-on AI query tools because it's built directly into the data layer rather than as a separate search interface.
+7 more capabilities