| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $25/mo |
| Capabilities | 8 decomposed | 15 decomposed |
| Times Matched | 0 | 0 |
Implements the Model Context Protocol (MCP) specification as a server that exposes Vapi's voice API capabilities through standardized MCP resources and tools. The server translates MCP client requests (from Claude or other MCP-compatible clients) into Vapi API calls, handling protocol serialization, request routing, and response marshaling. Uses stdio or HTTP transport to communicate with MCP clients, enabling seamless integration of voice AI capabilities into Claude and other LLM applications without custom integration code.
Unique: Provides native MCP server implementation specifically for Vapi's voice API, enabling Claude and other MCP clients to orchestrate phone calls and voice interactions without custom bridge code. Uses MCP's resource and tool discovery mechanisms to expose Vapi capabilities as first-class protocol primitives rather than generic function calls.
vs alternatives: Simpler than building custom Claude plugins or REST API wrappers because it leverages MCP's standardized tool schema and discovery, making Vapi capabilities immediately available to any MCP-compatible client without additional configuration.
Exposes Vapi's call creation and management APIs as discoverable MCP tools that clients can invoke to initiate phone calls, configure assistant behavior, and retrieve call status. The server translates MCP tool calls into authenticated Vapi REST API requests, handling credential management, request validation, and response transformation. Supports parameterized call configuration including assistant selection, phone number targeting, and custom variables, enabling dynamic voice interaction workflows driven by LLM reasoning.
Unique: Wraps Vapi's call APIs as discoverable MCP tools with full parameter introspection, allowing MCP clients to understand available call options and constraints before invocation. Handles authentication and request signing transparently, abstracting Vapi's REST API complexity behind the MCP tool interface.
vs alternatives: More discoverable and self-documenting than direct REST API calls because MCP tool schemas expose all available parameters and their types to the client, reducing integration friction compared to reading API documentation.
Exposes Vapi assistant configurations and metadata as MCP resources that clients can query and list, enabling dynamic assistant selection and configuration inspection. The server fetches assistant definitions from Vapi's API and presents them as structured MCP resources with full configuration details (voice settings, system prompts, tools, etc.). Clients can discover available assistants, inspect their capabilities, and reference them by ID when initiating calls, supporting dynamic workflow adaptation based on assistant features.
Unique: Leverages MCP's resource protocol to expose Vapi assistants as queryable entities rather than opaque IDs, enabling clients to discover and inspect assistant capabilities before use. Provides structured metadata access that mirrors Vapi's assistant configuration model.
vs alternatives: More integrated than requiring clients to make separate Vapi API calls to fetch assistant metadata because MCP resource discovery is built into the protocol, making assistant selection a first-class operation in the MCP interface.
Implements both stdio and HTTP transport layers for MCP protocol communication, allowing the server to operate in different deployment contexts (Claude Desktop via stdio, web applications via HTTP). The server handles transport-specific serialization (JSON-RPC 2.0 over stdio with newline delimiters, HTTP POST with JSON bodies), connection lifecycle management, and error handling. Clients can choose transport based on their environment, enabling the same MCP server implementation to work across desktop, web, and server-side applications.
Unique: Provides dual-transport implementation (stdio and HTTP) in a single server codebase, allowing deployment flexibility without code duplication. Uses transport abstraction layer to isolate protocol logic from transport-specific concerns, enabling easy addition of new transports.
vs alternatives: More flexible than single-transport MCP servers because it supports both local (stdio) and remote (HTTP) clients from the same implementation, reducing deployment complexity for teams needing multi-environment support.
Manages Vapi API authentication by accepting API keys through environment variables or configuration files and automatically injecting credentials into all outbound Vapi API requests. The server handles credential validation, error handling for authentication failures, and secure credential storage (avoiding hardcoding in logs or responses). Implements request signing and header injection for Vapi's REST API, abstracting authentication complexity from MCP clients.
Unique: Centralizes Vapi API authentication at the MCP server level, eliminating the need for MCP clients to handle credentials directly. Uses environment-based credential injection, following cloud-native security best practices.
vs alternatives: More secure than embedding API keys in client code or MCP tool definitions because credentials are managed server-side and never exposed to clients, reducing the attack surface for credential leakage.
Implements comprehensive error handling for Vapi API failures, translating Vapi-specific error responses into MCP-compatible error formats that clients can understand and act upon. The server catches HTTP errors, network failures, and API validation errors from Vapi, transforms them into MCP error responses with descriptive messages, and provides actionable error codes. Handles transient failures with retry logic (exponential backoff) for idempotent operations, improving reliability of voice call workflows.
Unique: Implements MCP-aware error transformation that converts Vapi API errors into MCP error responses with proper error codes and messages, enabling clients to handle errors using standard MCP error handling patterns. Includes automatic retry logic for transient failures.
vs alternatives: More resilient than direct Vapi API calls because it includes built-in retry logic and error transformation, reducing the burden on clients to implement their own error recovery strategies.
Validates incoming MCP tool calls against Vapi API parameter schemas before submitting requests, catching invalid configurations early and providing detailed validation errors to clients. The server enforces type checking, required field validation, and constraint checking (e.g., phone number format, assistant ID existence) at the MCP layer. Uses JSON Schema or similar validation mechanisms to ensure all requests conform to Vapi's API expectations, reducing failed API calls and improving user experience.
Unique: Implements schema-based parameter validation at the MCP layer before Vapi API submission, catching configuration errors early and providing detailed validation feedback. Uses declarative schema definitions to enforce Vapi API constraints.
vs alternatives: More efficient than discovering parameter errors through failed Vapi API calls because validation happens locally before network requests, reducing latency and API quota consumption.
Provides MCP tools to retrieve completed call transcripts, recordings, and structured results from Vapi, extracting and formatting call data for downstream processing. The server queries Vapi's call history API, transforms raw call data into structured formats (JSON with transcript, duration, cost, etc.), and exposes this data through MCP resources or tool results. Supports filtering and pagination for retrieving call history, enabling agents to analyze past interactions and extract insights.
Unique: Exposes Vapi call history and transcripts as structured MCP data, enabling clients to query and analyze call results without direct API access. Transforms raw Vapi call data into standardized formats suitable for downstream processing.
vs alternatives: More integrated than requiring clients to make separate Vapi API calls for transcripts because MCP provides a unified interface for call retrieval and result processing, reducing integration complexity.
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 @vapi-ai/mcp-server at 28/100. @vapi-ai/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.
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