Skip to content
AI & MLAuto-generatedScore: 46

Amazon Transcribe Service MCP Server

Amazon Transcribe is a sophisticated cloud-based automatic speech recognition (ASR) service provided by Amazon Web Services (AWS) that enables developers to convert speech-to-text accurately and at scale.

Quick Start Summary

The Amazon Transcribe Service MCP server is a Model Context Protocol bridge that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the Amazon Transcribe Service API through natural language. It exposes 10 API endpoints as callable tools, such as CreateCallAnalyticsCategory, CreateLanguageModel, CreateMedicalVocabulary, and more. No authentication is required — setup takes approximately 30 seconds. The server uses STDIO transport and can be installed by running npx -y @mcp/amazonaws-com-transcribe. This integration is sourced from the auto Amazon Transcribe Service OpenAPI specification (v2017-10-26) and has a quality score of 46/99 (fair documentation coverage).

10Endpointstools mapped
NoneAuthopen access
46/99Qualityfair
~30 secSetupno auth

Server Details

Category
AI & ML
Authentication
None
Endpoints
10 operations
Transport
STDIO
Spec Version
v2017-10-26
Install Command
npx -y @mcp/amazonaws-com-transcribe

Environment Variables

AMAZON_TRANSCRIBE_SERVICE_API_KEY

Example: your_amazon_transcribe_service_api_key

Top Endpoints

POST
/#X-Amz-Target=Transcribe.CreateCallAnalyticsCategory

CreateCallAnalyticsCategory

POST
/#X-Amz-Target=Transcribe.CreateLanguageModel

CreateLanguageModel

POST
/#X-Amz-Target=Transcribe.CreateMedicalVocabulary

CreateMedicalVocabulary

POST
/#X-Amz-Target=Transcribe.CreateVocabulary

CreateVocabulary

POST
/#X-Amz-Target=Transcribe.CreateVocabularyFilter

CreateVocabularyFilter

Own this API?

Verify ownership of this listing to control the description, configuration details, and documentation links. Choose between free manual verification or instant premium placement.

Option 1: Free Verification

Slow manual review. Requires creating a GitHub issue with verified documentation or domain verification.

  • • Verified badge on page
  • • Standard search sorting
  • • 2-3 business days review
Start Free Claim →
Instant & Boosted

Option 2: Featured Upgrade($9/mo)

Instant verification plus premium styling, featured badges, and directory placement boost.

  • • ★ Featured star & amber highlight border
  • • Top of directory search placement
  • • Instant activation via claim token

📖 Detailed MCP Integration Guide

A technical breakdown of capabilities, agent workflows, and security/configuration best practices.

Capabilities & Use Cases
Amazon Transcribe is a sophisticated cloud-based automatic speech recognition (ASR) service provided by Amazon Web Services (AWS) that enables developers to convert speech-to-text accurately and at scale. Its core capabilities extend beyond basic transcription to include three distinct, powerful batch processing modes: Standard, Medical, and Call Analytics. Standard transcription serves as the versatile foundation, supporting a wide array of languages and use cases from media captioning to customer service analysis. Medical Transcription is a specialized offering designed to understand and transcribe medical terminology with high accuracy, making it suitable for clinical notes and doctor-patient interactions. Call Analytics transcription is uniquely engineered to process multi-channel audio from contact centers, providing not just the transcript but also rich metadata like sentiment analysis, non-talk time, and interrupters, which are invaluable for quality assurance and business intelligence. This service is indispensable for enterprises in sectors like healthcare, customer service, legal, and media, enabling them to unlock actionable insights from vast amounts of audio data for compliance, training, and process optimization.
🤖AI Agent Value
When exposed as tools via the Model Context Protocol (MCP) to AI coding assistants such as Claude Desktop or Cursor, the Amazon Transcribe API transforms into a dynamic engine for intelligent automation within a developer's workflow. The AI agent gains the ability to directly interact with and manipulate transcription resources, moving beyond simple queries to perform complex, multi-step operations. For instance, a developer can instruct the AI to "create a custom vocabulary filter to redact sensitive customer information from all future standard transcription jobs," or "query the status of all running Medical Transcription jobs and alert me if any have been processing for over an hour." This integration empowers the developer to delegate routine management, data gathering, and configuration tasks to the AI, which can programmatically chain API calls to maintain vocabularies, monitor job pipelines, and organize call analytics categories, thereby accelerating development cycles and ensuring consistency.
💬Example Workflows
The practical workflows enabled by this MCP server integration are both numerous and impactful. An AI agent can be directed to "analyze the sentiment scores from the last week's Call Analytics jobs to identify a downward trend in customer satisfaction," or "automatically generate and apply a new medical vocabulary using terms extracted from a provided list of drug names to improve future transcription accuracy." Furthermore, it can manage the lifecycle of transcription assets by instructing the AI to "clean up resources by deleting all language models and vocabularies that haven't been used in the past 90 days," enforcing governance and cost control. The developer effectively gains a voice-driven or prompt-driven orchestrator for the Transcribe service, capable of performing detailed audits, updating configurations, and initiating batch processes through natural language instructions, which drastically reduces the cognitive load and manual coding required for service management.
🛡️Security & Auth
Critical security and configuration considerations are paramount when deploying this MCP server. Since the API uses "None" for authentication at the endpoint level shown, the actual access control is managed entirely through AWS Identity and Access Management (IAM). Developers must create a dedicated IAM user or role with the principle of least privilege, granting only the specific Transcribe permissions required (e.g., transcribe:CreateVocabulary, transcribe:ListJobs, transcribe:DeleteCallAnalyticsJob). Authentication to the MCP server itself must be secured with robust mechanisms, typically involving AWS access keys and secret access keys, which should be stored securely using environment variables or secret management services and never committed to source code. Furthermore, network policies should restrict access to the MCP server to trusted networks, and all interactions should be logged for audit trails. It is imperative to avoid providing overly permissive policies like transcribe:* and to regularly rotate credentials, ensuring that the AI assistant's powerful programmatic access is tightly controlled and monitored.

Similar APIs

Other APIs in the AI & ML category.

OpenAI API

Generate text, images, and embeddings. Integrate GPT models and DALL-E into your AI agent.

API Key

Anthropic API

Access Claude AI models for text generation, analysis, and code assistance through the Anthropic API.

API Key

OpenAI API

The OpenAI API, developed and maintained by OpenAI, provides programmatic access to a suite of advanced artificial intelligence capabilities centered around large language models (LLMs). Its core functions enable developers to integrate state-of-the-art natural language processing and generation into applications. Key endpoints support text generation (completions, chat completions), content transformation (edits, classifications), semantic analysis (embeddings), and multimodal processing (audio transcriptions and translations). The API serves a broad spectrum of users, from individual developers and startups building conversational agents or content tools to large enterprises automating complex workflows, enhancing customer support, conducting sentiment analysis on large text corpora, or generating synthetic data for training. Use cases span consumer applications like intelligent writing assistants and enterprise-grade solutions for automated document summarization, code generation, and multilingual communication platforms.

Amazon CodeGuru Profiler

Amazon CodeGuru Profiler is an advanced application performance profiling service provided by Amazon Web Services (AWS). It continuously collects runtime performance data—such as CPU utilization, memory allocation, and thread contention—from live production applications, then analyzes this data using machine learning algorithms to pinpoint performance bottlenecks and inefficiencies. The API serves as the programmatic interface for managing the profiling lifecycle, allowing developers to create and configure profiling groups, adjust agent settings, retrieve performance metrics and findings, and manage notification configurations. Enterprise use cases include optimizing microservice latency in high-traffic systems, reducing cloud compute costs by identifying inefficient code paths, and maintaining application health in continuous deployment pipelines where performance regressions must be detected early. For development teams, it provides actionable insights to guide code optimization efforts based on real-world usage rather than synthetic benchmarks.

Related MCP Server Integrations

OpenAI API MCP Setup

Generate text, images, and embeddings. Integrate GPT models and DALL-E into your AI agent.

AI & MLConfigure →

Anthropic API MCP Setup

Access Claude AI models for text generation, analysis, and code assistance through the Anthropic API.

AI & MLConfigure →

OpenAI API MCP Setup

The OpenAI API, developed and maintained by OpenAI, provides programmatic access to a suite of advanced artificial intelligence capabilities centered around large language models (LLMs). Its core functions enable developers to integrate state-of-the-art natural language processing and generation into applications. Key endpoints support text generation (completions, chat completions), content transformation (edits, classifications), semantic analysis (embeddings), and multimodal processing (audio transcriptions and translations). The API serves a broad spectrum of users, from individual developers and startups building conversational agents or content tools to large enterprises automating complex workflows, enhancing customer support, conducting sentiment analysis on large text corpora, or generating synthetic data for training. Use cases span consumer applications like intelligent writing assistants and enterprise-grade solutions for automated document summarization, code generation, and multilingual communication platforms.

AI & MLConfigure →

Amazon CodeGuru Profiler MCP Setup

Amazon CodeGuru Profiler is an advanced application performance profiling service provided by Amazon Web Services (AWS). It continuously collects runtime performance data—such as CPU utilization, memory allocation, and thread contention—from live production applications, then analyzes this data using machine learning algorithms to pinpoint performance bottlenecks and inefficiencies. The API serves as the programmatic interface for managing the profiling lifecycle, allowing developers to create and configure profiling groups, adjust agent settings, retrieve performance metrics and findings, and manage notification configurations. Enterprise use cases include optimizing microservice latency in high-traffic systems, reducing cloud compute costs by identifying inefficient code paths, and maintaining application health in continuous deployment pipelines where performance regressions must be detected early. For development teams, it provides actionable insights to guide code optimization efforts based on real-world usage rather than synthetic benchmarks.

AI & MLConfigure →

Amazon CodeGuru Reviewer MCP Setup

The Amazon CodeGuru Reviewer API is a powerful programmatic interface to Amazon's automated code analysis service, designed to elevate code quality and developer productivity. This API exposes the core functionalities of a managed service that combines deep static analysis, machine learning models trained on vast code repositories, and pattern recognition to identify complex defects, security vulnerabilities, and non-idiomatic code patterns that are often missed in manual reviews. Specifically targeting Java and Python codebases, CodeGuru Reviewer analyzes code changes submitted through integrated repositories like AWS CodeCommit, GitHub, or Bitbucket, and generates actionable recommendations. Its primary enterprise use cases are integrated into continuous integration and continuous delivery (CI/CD) pipelines for automated, mandatory code quality gates; conducting security and compliance audits on critical application code; and providing scalable, consistent feedback during the pull request process, thereby reducing the burden on human reviewers and accelerating safe code deployments.

AI & MLConfigure →