What can @vapi-ai/mcp-server do?

mcp protocol server implementation for vapi voice api, vapi call initiation and management through mcp tools, assistant configuration and metadata exposure via mcp resources, stdio and http transport abstraction for mcp protocol communication, vapi api authentication and credential management, error handling and response transformation for vapi api failures, request validation and parameter schema enforcement, call transcript and result retrieval with structured data extraction

@vapi-ai/mcp-server

MCP ServerFree

Vapi MCP Server

Open Source

/ 100

8 capabilities

Capabilities8 decomposed

mcp protocol server implementation for vapi voice api

Medium confidence

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.

Solves for

I want to use Vapi's voice API directly from Claude without writing custom API integration codeI need to expose Vapi voice capabilities as standardized MCP tools that any MCP client can discover and useI want to build a voice-enabled AI agent that can make phone calls through Vapi using Claude as the reasoning engine

Best for

AI developers building voice agents with Claude as the LLM backbone

Teams integrating Vapi voice capabilities into existing MCP-compatible applications

Builders prototyping multi-modal AI systems combining text reasoning with voice interaction

Requires

Node.js 16+ or compatible JavaScript runtime

Vapi API key (obtainable from https://dashboard.vapi.ai)

MCP client that supports stdio or HTTP transport (Claude Desktop, custom MCP client, etc.)

Limitations

Requires Vapi API key and valid Vapi account with active credits or subscription

MCP protocol overhead adds latency compared to direct REST API calls (typically 50-150ms per round-trip)

Limited to Vapi's supported voice models and features — no custom voice synthesis or advanced audio processing

What makes it 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.

vapi call initiation and management through mcp tools

Medium confidence

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.

Solves for

I want to programmatically initiate a phone call to a specific number using a Vapi assistant from within a Claude conversationI need to configure call parameters (assistant, phone number, variables) dynamically based on LLM reasoning before making the callI want to retrieve the status and results of a Vapi call that was initiated earlier in an agent workflow

Best for

AI agents that need to make outbound calls as part of multi-step workflows

Customer service automation systems using voice as a primary interaction channel

Developers building voice-first AI applications with Claude as the orchestration layer

Requires

Valid Vapi API key with call-making permissions

Pre-configured Vapi assistant (created in Vapi dashboard)

Destination phone number in E.164 format or Vapi-compatible format

Limitations

Call initiation is asynchronous — responses return call IDs immediately but actual call completion happens out-of-band

No built-in polling or webhook integration — clients must manually query call status or implement external polling

Limited to Vapi's supported phone numbers and regions — international calling subject to Vapi's carrier limitations

What makes it 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.

assistant configuration and metadata exposure via mcp resources

Medium confidence

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.

Solves for

I want to list all available Vapi assistants and their configurations to choose the right one for a taskI need to inspect an assistant's system prompt, voice settings, and available tools before using it in a callI want to dynamically select an assistant based on the conversation context or user requirements

Best for

Multi-assistant voice systems where assistant selection is context-dependent

Developers building admin or monitoring dashboards for Vapi assistants

AI agents that need to reason about which assistant is best suited for a given task

Requires

Valid Vapi API key with read permissions for assistants

MCP client that supports resource listing and retrieval

Pre-existing Vapi assistants in the account

Limitations

Resource listing is read-only — assistant creation and modification must happen outside the MCP server (in Vapi dashboard)

Metadata refresh is not real-time — changes to assistants in Vapi dashboard may take time to propagate to MCP clients

No filtering or search capabilities — clients must enumerate all assistants and filter client-side

What makes it 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.

stdio and http transport abstraction for mcp protocol communication

Medium confidence

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.

Solves for

I want to run the Vapi MCP server in Claude Desktop using stdio transport for local integrationI need to deploy the Vapi MCP server as an HTTP service that web applications can connect toI want to support both desktop and web clients connecting to the same MCP server implementation

Best for

Developers deploying MCP servers in heterogeneous environments (desktop + web + server)

Teams building Claude Desktop integrations that also need web accessibility

Infrastructure teams standardizing on MCP for AI tool integration across platforms

Requires

Node.js runtime with stdio and HTTP server capabilities

For stdio: parent process that can spawn and manage child processes (Claude Desktop, custom MCP client)

For HTTP: network accessibility and optional TLS certificates for production deployments

Limitations

Stdio transport has inherent latency and bandwidth limitations — not suitable for high-frequency or large-payload operations

HTTP transport requires network connectivity and introduces additional security considerations (CORS, authentication, TLS)

No automatic transport selection — clients must explicitly configure which transport to use

What makes it 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.

vapi api authentication and credential management

Medium confidence

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.

Solves for

I want to securely provide my Vapi API key to the MCP server without hardcoding it in client codeI need the MCP server to automatically authenticate all Vapi API calls on my behalfI want to rotate or update my Vapi credentials without restarting the MCP server

Best for

Production deployments requiring secure credential management

Teams using environment-based configuration for secrets (Docker, Kubernetes, CI/CD)

Developers building multi-tenant systems where credentials vary per request

Requires

Valid Vapi API key (obtainable from https://dashboard.vapi.ai)

Environment variable VAPI_API_KEY or configuration file with API key

Secure environment for server deployment (not exposed to untrusted users)

Limitations

Credentials must be provided at server startup — no dynamic credential injection per request

No built-in credential rotation or expiration handling — relies on external secret management systems

API key is stored in memory — vulnerable to memory dumps or process inspection if server is compromised

What makes it 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.

error handling and response transformation for vapi api failures

Medium confidence

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.

Solves for

I want clear error messages when a Vapi API call fails so I can debug integration issuesI need the MCP server to automatically retry transient failures (network timeouts, rate limits) without client interventionI want to distinguish between recoverable errors (rate limit) and permanent errors (invalid assistant ID) in my client code

Best for

Production voice agents requiring high reliability and graceful degradation

Teams building error recovery logic into AI workflows

Developers debugging integration issues between Claude and Vapi

Requires

Vapi API that returns structured error responses

MCP client that can handle error responses in JSON-RPC 2.0 error format

Limitations

Retry logic only applies to idempotent operations — non-idempotent calls (initiating calls) may fail without retry to avoid duplicate operations

Error messages are limited to Vapi's API response format — may lack context about the original MCP request

No exponential backoff configuration — uses fixed retry strategy that may not suit all use cases

What makes it 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.

request validation and parameter schema enforcement

Medium confidence

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.

Solves for

I want validation errors for invalid parameters before the MCP server attempts to call Vapi's APII need to understand what parameters are required and what formats are expected for each Vapi operationI want to catch configuration errors early in development rather than discovering them in production

Best for

Development teams building voice agents with strict quality requirements

Automated testing frameworks that need to validate MCP tool parameters

Developers new to Vapi who need guidance on valid parameter formats

Requires

JSON Schema definitions for Vapi API parameters

MCP client that can handle validation error responses

Limitations

Validation is limited to schema-level checks — cannot validate business logic (e.g., whether an assistant actually exists without API call)

Schema definitions must be maintained in sync with Vapi's API — outdated schemas may reject valid parameters

Validation errors may be verbose and technical — not always user-friendly for non-technical users

What makes it 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.

call transcript and result retrieval with structured data extraction

Medium confidence

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.

Solves for

I want to retrieve the transcript of a completed Vapi call to analyze what was discussedI need to extract structured data from call results (duration, cost, outcome) for logging or analyticsI want to list recent calls and filter them by date, assistant, or phone number for reporting

Best for

Voice agents that need to analyze call outcomes and extract insights

Analytics and reporting systems tracking voice interaction metrics

Quality assurance teams reviewing call transcripts and agent performance

Requires

Completed Vapi calls with call IDs

Vapi API key with read permissions for call history

MCP client that can handle structured data responses

Limitations

Transcript retrieval is asynchronous — transcripts may not be immediately available after call completion (typically 5-30 seconds delay)

No real-time transcript streaming — only post-call transcript retrieval is supported

Transcript accuracy depends on Vapi's speech-to-text engine — may contain errors or misrecognitions

What makes it 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.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with @vapi-ai/mcp-server, ranked by overlap. Discovered automatically through the match graph.

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mcp server instantiation with vapi voice api integrationbidirectional mcp protocol communication with stdio/http transportresource exposure for voice call metadata and configurationauthentication and credential management for vapi api access

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MCP Server36

@z_ai/mcp-server

MCP Server for Z.AI - A Model Context Protocol server that provides AI capabilities

mcp server protocol implementation with ai model integration

1 shared capability

Best For

✓AI developers building voice agents with Claude as the LLM backbone
✓Teams integrating Vapi voice capabilities into existing MCP-compatible applications
✓Builders prototyping multi-modal AI systems combining text reasoning with voice interaction
✓AI agents that need to make outbound calls as part of multi-step workflows
✓Customer service automation systems using voice as a primary interaction channel
✓Developers building voice-first AI applications with Claude as the orchestration layer
✓Multi-assistant voice systems where assistant selection is context-dependent
✓Developers building admin or monitoring dashboards for Vapi assistants

Known Limitations

⚠Requires Vapi API key and valid Vapi account with active credits or subscription
⚠MCP protocol overhead adds latency compared to direct REST API calls (typically 50-150ms per round-trip)
⚠Limited to Vapi's supported voice models and features — no custom voice synthesis or advanced audio processing
⚠Stdio transport has bandwidth limitations for large audio payloads or high-frequency tool calls
⚠Call initiation is asynchronous — responses return call IDs immediately but actual call completion happens out-of-band
⚠No built-in polling or webhook integration — clients must manually query call status or implement external polling

Requirements

Node.js 16+ or compatible JavaScript runtimeVapi API key (obtainable from https://dashboard.vapi.ai)MCP client that supports stdio or HTTP transport (Claude Desktop, custom MCP client, etc.)@vapi-ai/mcp-server package from npmValid Vapi API key with call-making permissionsPre-configured Vapi assistant (created in Vapi dashboard)Destination phone number in E.164 format or Vapi-compatible formatMCP client capable of invoking tools with structured parameters

Input / Output

Accepts: MCP tool call requests (JSON-RPC 2.0 format), Vapi API parameters (phone numbers, assistant configs, call settings), Text prompts and conversation context from MCP clients, MCP tool parameters: assistant ID, phone number, custom variables, call configuration options, Vapi API authentication credentials (API key passed via environment or configuration), MCP resource queries: list all assistants, get specific assistant by ID, MCP protocol messages in JSON-RPC 2.0 format, Transport-specific framing (newline-delimited for stdio, HTTP POST body for HTTP), Vapi API key as environment variable or configuration, Vapi API error responses (HTTP status codes, error bodies), MCP tool call parameters in JSON format, Call ID for specific call retrieval, Filter parameters: date range, assistant ID, phone number, status

Produces: MCP tool results (JSON-RPC 2.0 responses), Call status and metadata (call IDs, duration, transcripts), Structured call logs and analytics data, Call initiation response: call ID, status, timestamp, Call status queries: current state, duration, transcript (when available), Call metadata: assistant used, phone number, variables, cost, Structured assistant metadata: ID, name, voice settings, system prompt, tools, model configuration, Assistant list with summary information for discovery, MCP protocol responses in JSON-RPC 2.0 format, Transport-specific response framing (stdout for stdio, HTTP response body for HTTP), Authenticated Vapi API requests with Authorization headers, MCP error responses with error codes and descriptive messages, Retry attempts with exponential backoff for transient failures, Validation success (request proceeds to Vapi API), Validation error responses with detailed error messages and field-level feedback, Structured call data: ID, timestamp, duration, cost, status, Call transcript with speaker labels and timing, Call metadata: assistant used, phone number, variables, outcome

UnfragileRank

Adoption16%(30% weight)

Quality17%(25% weight)

Ecosystem60%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

8 capabilities

Visit @vapi-ai/mcp-server→

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Capabilities8 decomposed

mcp protocol server implementation for vapi voice api

Medium confidence

Solves for

Best for

AI developers building voice agents with Claude as the LLM backbone

Teams integrating Vapi voice capabilities into existing MCP-compatible applications

Builders prototyping multi-modal AI systems combining text reasoning with voice interaction

Requires

Node.js 16+ or compatible JavaScript runtime

Vapi API key (obtainable from https://dashboard.vapi.ai)

MCP client that supports stdio or HTTP transport (Claude Desktop, custom MCP client, etc.)

Limitations

Requires Vapi API key and valid Vapi account with active credits or subscription

MCP protocol overhead adds latency compared to direct REST API calls (typically 50-150ms per round-trip)

Limited to Vapi's supported voice models and features — no custom voice synthesis or advanced audio processing

What makes it unique

vs alternatives

vapi call initiation and management through mcp tools

Medium confidence

Solves for

Best for

AI agents that need to make outbound calls as part of multi-step workflows

Customer service automation systems using voice as a primary interaction channel

Developers building voice-first AI applications with Claude as the orchestration layer

Requires

Valid Vapi API key with call-making permissions

Pre-configured Vapi assistant (created in Vapi dashboard)

Destination phone number in E.164 format or Vapi-compatible format

Limitations

Call initiation is asynchronous — responses return call IDs immediately but actual call completion happens out-of-band

No built-in polling or webhook integration — clients must manually query call status or implement external polling

Limited to Vapi's supported phone numbers and regions — international calling subject to Vapi's carrier limitations

What makes it unique

vs alternatives

assistant configuration and metadata exposure via mcp resources

Medium confidence

Solves for

Best for

Multi-assistant voice systems where assistant selection is context-dependent

Developers building admin or monitoring dashboards for Vapi assistants

AI agents that need to reason about which assistant is best suited for a given task

Requires

Valid Vapi API key with read permissions for assistants

MCP client that supports resource listing and retrieval

Pre-existing Vapi assistants in the account

Limitations

Resource listing is read-only — assistant creation and modification must happen outside the MCP server (in Vapi dashboard)

Metadata refresh is not real-time — changes to assistants in Vapi dashboard may take time to propagate to MCP clients

No filtering or search capabilities — clients must enumerate all assistants and filter client-side

What makes it unique

vs alternatives

stdio and http transport abstraction for mcp protocol communication

Medium confidence

Solves for

Best for

Developers deploying MCP servers in heterogeneous environments (desktop + web + server)

Teams building Claude Desktop integrations that also need web accessibility

Infrastructure teams standardizing on MCP for AI tool integration across platforms

Requires

Node.js runtime with stdio and HTTP server capabilities

For stdio: parent process that can spawn and manage child processes (Claude Desktop, custom MCP client)

For HTTP: network accessibility and optional TLS certificates for production deployments

Limitations

Stdio transport has inherent latency and bandwidth limitations — not suitable for high-frequency or large-payload operations

HTTP transport requires network connectivity and introduces additional security considerations (CORS, authentication, TLS)

No automatic transport selection — clients must explicitly configure which transport to use

What makes it unique

vs alternatives

vapi api authentication and credential management

Medium confidence

Solves for

Best for

Production deployments requiring secure credential management

Teams using environment-based configuration for secrets (Docker, Kubernetes, CI/CD)

Developers building multi-tenant systems where credentials vary per request

Requires

Valid Vapi API key (obtainable from https://dashboard.vapi.ai)

Environment variable VAPI_API_KEY or configuration file with API key

Secure environment for server deployment (not exposed to untrusted users)

Limitations

Credentials must be provided at server startup — no dynamic credential injection per request

No built-in credential rotation or expiration handling — relies on external secret management systems

API key is stored in memory — vulnerable to memory dumps or process inspection if server is compromised

What makes it unique

vs alternatives

error handling and response transformation for vapi api failures

Medium confidence

Solves for

Best for

Production voice agents requiring high reliability and graceful degradation

Teams building error recovery logic into AI workflows

Developers debugging integration issues between Claude and Vapi

Requires

Vapi API that returns structured error responses

MCP client that can handle error responses in JSON-RPC 2.0 error format

Limitations

Retry logic only applies to idempotent operations — non-idempotent calls (initiating calls) may fail without retry to avoid duplicate operations

Error messages are limited to Vapi's API response format — may lack context about the original MCP request

No exponential backoff configuration — uses fixed retry strategy that may not suit all use cases

What makes it unique

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.

request validation and parameter schema enforcement

Medium confidence

Solves for

Best for

Development teams building voice agents with strict quality requirements

Automated testing frameworks that need to validate MCP tool parameters

Developers new to Vapi who need guidance on valid parameter formats

Requires

JSON Schema definitions for Vapi API parameters

MCP client that can handle validation error responses

Limitations

Validation is limited to schema-level checks — cannot validate business logic (e.g., whether an assistant actually exists without API call)

Schema definitions must be maintained in sync with Vapi's API — outdated schemas may reject valid parameters

Validation errors may be verbose and technical — not always user-friendly for non-technical users

What makes it unique

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.

call transcript and result retrieval with structured data extraction

Medium confidence

Solves for

Best for

Voice agents that need to analyze call outcomes and extract insights

Analytics and reporting systems tracking voice interaction metrics

Quality assurance teams reviewing call transcripts and agent performance

Requires

Completed Vapi calls with call IDs

Vapi API key with read permissions for call history

MCP client that can handle structured data responses

Limitations

Transcript retrieval is asynchronous — transcripts may not be immediately available after call completion (typically 5-30 seconds delay)

No real-time transcript streaming — only post-call transcript retrieval is supported

Transcript accuracy depends on Vapi's speech-to-text engine — may contain errors or misrecognitions

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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@vapi-ai/mcp-server

Capabilities8 decomposed

mcp protocol server implementation for vapi voice api

vapi call initiation and management through mcp tools

assistant configuration and metadata exposure via mcp resources

stdio and http transport abstraction for mcp protocol communication

vapi api authentication and credential management

error handling and response transformation for vapi api failures

request validation and parameter schema enforcement

call transcript and result retrieval with structured data extraction

Related Artifactssharing capabilities

@vapi-ai/mcp-server

Carbon Voice

@ivotoby/openapi-mcp-server

@vantasdk/vanta-mcp-server

@voltagent/mcp-server

@z_ai/mcp-server

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

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@vapi-ai/mcp-server

Capabilities8 decomposed

mcp protocol server implementation for vapi voice api

vapi call initiation and management through mcp tools

assistant configuration and metadata exposure via mcp resources

stdio and http transport abstraction for mcp protocol communication

vapi api authentication and credential management

error handling and response transformation for vapi api failures

request validation and parameter schema enforcement

call transcript and result retrieval with structured data extraction

Related Artifactssharing capabilities

@vapi-ai/mcp-server

Carbon Voice

@ivotoby/openapi-mcp-server

@vantasdk/vanta-mcp-server

@voltagent/mcp-server

@z_ai/mcp-server

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

Package Details

About

Categories

Alternatives to @vapi-ai/mcp-server

Are you the builder of @vapi-ai/mcp-server?

Get the weekly brief

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