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Twitch▌

mtane0412

by mtane0412

Unlock Twitch analytics and insights with integrated API: channel info, streams, game data, clips, chat, and user profil

Integrates with Twitch API to provide channel info, stream details, game data, user profiles, clips, chat settings, and video comments for building Twitch-related tools and analytics platforms.

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Requires Twitch API credentialsAccess to both Helix API and GraphQL data

best for

  • / Building Twitch analytics dashboards
  • / Creating stream monitoring tools
  • / Developing Twitch chatbots or extensions
  • / Analyzing streamer performance data

capabilities

  • / Get channel information and profiles
  • / Retrieve stream details and viewer counts
  • / Search games and categories
  • / Fetch clips from channels
  • / Access chat settings and badges
  • / Get video comments from archived streams

what it does

Connects to Twitch API to fetch channel information, stream data, user profiles, clips, chat settings, and video comments. Requires Twitch API credentials to access Twitch's data programmatically.

about

Twitch is a community-built MCP server published by mtane0412 that provides AI assistants with tools and capabilities via the Model Context Protocol. Unlock Twitch analytics and insights with integrated API: channel info, streams, game data, clips, chat, and user profil It is categorized under other.

how to install

You can install Twitch in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Twitch is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Twitch MCP Server

A Model Context Protocol (MCP) server that interacts with the Twitch API. This server utilizes the Twitch Helix API to retrieve channel information, stream details, game data, and more.

Features

  • Get channel information (profile, description, creation date, etc.)
  • Get stream information (title, game, viewer count, start time, etc.)
  • Get list of top games
  • Search categories/games
  • Search channels
  • Get live streams (filterable by game and language)
  • Get global emotes
  • Get global chat badges
  • Get user information
  • Get clips from a channel
  • Get chat settings
  • Get videos from a specified channel
  • Get comments from archived videos (using GraphQL API)

Prerequisites

  • Node.js (v18 or higher recommended)
  • Twitch Developer Account
  • Twitch API Client ID and Client Secret
  • Twitch GraphQL Client ID (for video comments feature)

Installation

Install the package using npm:

npm install @mtane0412/twitch-mcp-server

Configuration

  1. Create a new application in the Twitch Developer Console

  2. Set the following environment variables:

# macOS/Linux
export TWITCH_CLIENT_ID="your_client_id"
export TWITCH_CLIENT_SECRET="your_client_secret"

# Windows (PowerShell)
$env:TWITCH_CLIENT_ID="your_client_id"
$env:TWITCH_CLIENT_SECRET="your_client_secret"

Alternatively, you can create a .env file:

TWITCH_CLIENT_ID=your_client_id
TWITCH_CLIENT_SECRET=your_client_secret

Usage

After installation, you can start using the server by running:

npx @mtane0412/twitch-mcp-server

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:

npm run inspect

The Inspector will provide a URL to access debugging tools in your browser.

License

MIT License

FAQ

What is the Twitch MCP server?
Twitch is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Twitch?
This profile displays 43 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.

Use Cases▌

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

✓

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

✓

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

✓

Complete complex tasks end-to-end without manual steps

Implementation Guide▌

Prerequisites

  • ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • ›Basic understanding of MCP architecture and capabilities
  • ›Access credentials for integrated services (if required)
  • ›Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • ⚠MCP server not loading: Check config syntax, verify installation
  • ⚠Connection errors: Check network, firewall, credentials
  • ⚠Feature not working: Read server docs, check required parameters
  • ⚠Performance issues: Monitor resource usage, check for network latency
  • ⚠Conflicts with other servers: Check port assignments, namespace collisions

Best Practices▌

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • −Don't grant overly permissive access to MCP servers
  • −Don't skip reading security considerations in docs
  • −Don't expose sensitive data without proper controls
  • −Don't run untrusted MCP servers without code review
  • −Don't ignore error messages—investigate root cause

💡 Pro Tips

  • ★Combine multiple MCP servers for powerful workflows
  • ★Create custom MCP servers for your specific needs
  • ★Share successful configurations with team
  • ★Use MCP inspector for debugging
  • ★Join MCP community for tips and troubleshooting

Technical Details▌

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This▌

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration▌

  • →Tool composition: Chain multiple MCP tools in workflows
  • →Context augmentation: Provide AI with relevant external data
  • →Action delegation: Let AI execute tasks on external systems
  • →Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.5★★★★★43 reviews
  • ★★★★★Arya Iyer· Dec 28, 2024

    Useful MCP listing: Twitch is the kind of server we cite when onboarding engineers to host + tool permissions.

  • ★★★★★Arjun Chen· Dec 16, 2024

    Strong directory entry: Twitch surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • ★★★★★Ganesh Mohane· Dec 4, 2024

    I recommend Twitch for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • ★★★★★Mei Kapoor· Dec 4, 2024

    Twitch is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • ★★★★★Sakshi Patil· Nov 23, 2024

    According to our notes, Twitch benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • ★★★★★Neel Agarwal· Nov 23, 2024

    We evaluated Twitch against two servers with overlapping tools; this profile had the clearer scope statement.

  • ★★★★★Chaitanya Patil· Oct 14, 2024

    Twitch is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • ★★★★★Naina Okafor· Oct 14, 2024

    I recommend Twitch for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • ★★★★★Chinedu Jackson· Sep 25, 2024

    I recommend Twitch for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • ★★★★★Piyush G· Sep 21, 2024

    Twitch is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

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