| 0 |
| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $25/mo |
| Capabilities | 12 decomposed | 15 decomposed |
| Times Matched | 0 | 0 |
Exposes Apify Actors as MCP tools that Claude and other MCP clients can invoke directly. Implements the Model Context Protocol specification to translate tool-call requests into Apify Actor API calls, handling authentication, payload marshaling, and result streaming back to the client. Uses MCP's standardized tool schema to describe Actor inputs and outputs, enabling seamless integration with LLM-based agents without custom integration code.
Unique: Native MCP server implementation that bridges Apify's Actor execution model directly into the Model Context Protocol, allowing LLMs to treat Apify Actors as first-class tools without custom adapters or API gateway code
vs alternatives: Tighter integration than REST API wrappers because it implements MCP's tool schema natively, enabling Claude to understand Actor capabilities and constraints at protocol level rather than through generic function descriptions
Automatically discovers all Actors available in an Apify account and generates MCP-compliant tool schemas describing their inputs, outputs, and execution parameters. Introspects Actor metadata (name, description, input schema, expected output format) from Apify's API and transforms it into MCP ToolDefinition objects that LLM clients can parse and present to users. Caches schema information to avoid repeated API calls during agent planning phases.
Unique: Implements automatic schema extraction from Apify's Actor metadata API, converting Apify's input/output schema format into MCP ToolDefinition objects with zero manual configuration per Actor
vs alternatives: Eliminates manual tool registration compared to generic MCP servers — new Actors are automatically discoverable without updating configuration files or restarting the server
Propagates execution context (user ID, session ID, request ID, custom metadata) through Actor invocations, enabling traceability and correlation across distributed executions. Injects context into Actor environment variables and logs, allowing Actors to include context in their output for audit trails. Supports custom metadata tags that agents can attach to Actor runs for filtering and analysis.
Unique: Implements context propagation as a first-class MCP feature, automatically injecting execution context into Actor invocations without requiring manual environment variable management
vs alternatives: More reliable than manual context passing because context is propagated at the MCP layer, ensuring consistency across all Actor invocations in a workflow
Enforces rate limits on Actor invocations to prevent overwhelming Apify infrastructure or exceeding account concurrency limits. Implements token-bucket rate limiting with configurable rates (e.g., max 10 concurrent Actors, max 100 invocations per minute). Queues excess invocations and executes them as capacity becomes available, providing agents with visibility into queue status and estimated wait times.
Unique: Implements token-bucket rate limiting at the MCP layer, preventing agents from exceeding Apify concurrency limits without requiring manual coordination or external rate limiting services
vs alternatives: More effective than agent-side rate limiting because it operates at the MCP server level, protecting shared Apify infrastructure from any single agent's runaway behavior
Streams Actor execution results back to the MCP client in real-time, handling pagination for large datasets and chunking output into manageable pieces. Implements streaming via MCP's text content blocks, allowing long-running Actors to return partial results as they complete. Automatically handles Apify's dataset pagination API, fetching results in batches and presenting them to the client without requiring manual offset/limit management.
Unique: Implements MCP streaming semantics for Apify dataset results, automatically handling pagination and chunking to present large result sets as continuous streams rather than monolithic responses
vs alternatives: More efficient than polling-based approaches because it uses Apify's native dataset API for pagination, reducing API calls and enabling true streaming rather than buffering entire results
Tracks Actor execution state (running, succeeded, failed, timed out) and exposes status information to the MCP client via tool results and optional status callbacks. Polls Apify's Actor run API at configurable intervals to detect completion, failures, and resource constraints. Provides structured error messages including failure reasons, logs, and resource usage metrics that help LLM agents understand why an Actor failed and decide whether to retry or escalate.
Unique: Implements polling-based status tracking integrated into MCP tool results, allowing LLM agents to await Actor completion and receive structured failure information without custom monitoring infrastructure
vs alternatives: Simpler than building custom monitoring dashboards because status is embedded in tool results, enabling agents to make decisions based on execution outcomes without external observability tools
Validates Actor input parameters against the Actor's declared input schema before execution, catching configuration errors early and providing detailed validation error messages. Uses JSON schema validation to check required fields, type constraints, and value ranges. Returns validation errors to the LLM client before attempting execution, allowing agents to correct inputs or request user clarification rather than wasting Actor execution time on invalid inputs.
Unique: Integrates JSON schema validation directly into the MCP tool invocation path, rejecting invalid inputs before they reach Apify rather than relying on Actor-side validation
vs alternatives: Faster feedback than Actor-side validation because errors are caught at the MCP layer, saving network round-trips and Actor execution time for obviously invalid inputs
Enables sequential or parallel execution of multiple Actors within a single agent workflow, with output from one Actor automatically passed as input to the next. Implements dependency tracking to ensure Actors execute in the correct order, and provides utilities for transforming output from one Actor into the input format expected by the next. Handles error propagation — if an Actor in a chain fails, subsequent Actors are skipped unless the agent explicitly implements retry logic.
Unique: Provides MCP-native orchestration patterns for Apify Actors, allowing agents to compose Actors into workflows without external orchestration tools like Airflow or Prefect
vs alternatives: Simpler than dedicated workflow engines because orchestration logic lives in the agent itself, eliminating the need to learn separate DSLs or maintain separate pipeline definitions
+4 more capabilities
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 @apify/actors-mcp-server at 37/100. @apify/actors-mcp-server 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