Zendesk Help Center MCP Server

Zendesk Help Center MCP Server

Enables AI agents to browse, search, and fetch documentation from any public Zendesk Help Center via a read-only API.

Category
Visit Server

README

Zendesk Help Center MCP Server

A read-only MCP (Model Context Protocol) server for querying public Zendesk Help Center APIs. Lets AI agents browse, search, and fetch documentation from any public Zendesk Help Center.

Features

  • Browse Help Center structure (categories, sections, articles)
  • Fetch article content as clean markdown
  • Search articles by keyword
  • Browse community posts and comments
  • Locale-aware (filter by language)
  • HTTP response caching (7-day TTL via hishel)
  • Works with any public Zendesk Help Center (no auth required)

Installation

Requires Python 3.10+.

git clone <repo-url>
cd zendesk
uv sync

Or with pip:

pip install -e .

Configuration

Claude Code

Add a .mcp.json file to your project root (or any project that needs Zendesk HC access):

{
  "mcpServers": {
    "zendesk-hc": {
      "command": "uv",
      "args": ["--directory", "/path/to/zendesk-hc-mcp", "run", "zendesk-hc-mcp"]
    }
  }
}

Replace /path/to/zendesk-hc-mcp with the absolute path to where you cloned this repo. Do not use ~ — tilde expansion does not work in MCP config.

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "zendesk-hc": {
      "command": "uv",
      "args": ["--directory", "/path/to/zendesk-hc-mcp", "run", "zendesk-hc-mcp"]
    }
  }
}

Note: uv must be on PATH for the MCP host process to find it.

Available Tools

All tools take subdomain as the first parameter (e.g., "support.zendesk.com"). All tools accept force_refresh=True to bypass the cache. Most tools accept an optional locale parameter (e.g., "en-us", "ru") to filter by language.

Structure Browsing

Tool Description
list_categories(subdomain, locale?) Entry point: shows locales, categories, and section counts
list_sections(subdomain, category_id?, locale?) Sections, optionally filtered by category

Content Retrieval

Tool Description
get_article(subdomain, article_id) Single article with full markdown body
get_articles(subdomain, article_ids) Batch fetch multiple articles concurrently
get_section_articles(subdomain, section_id, locale?, include_body?, max_articles?) TOC by default; set include_body=True for full text
search_articles(subdomain, query, locale?, limit?) Keyword search with snippets (no bodies)

Community

Tool Description
list_community_topics(subdomain) Forum topics
list_community_posts(subdomain, topic_id?, limit?) Posts with body previews and comment counts
get_community_post(subdomain, post_id) Full post with comments

Usage Examples

Discover a Help Center's structure:

list_categories("support.zendesk.com")

Browse sections in a category:

list_sections("support.zendesk.com", category_id=360001006608)

Fetch all articles in a section (TOC only):

get_section_articles("support.zendesk.com", section_id=360000031847)

Fetch articles with full content:

get_section_articles("support.zendesk.com", section_id=360000031847, include_body=True, max_articles=5)

Search for articles:

search_articles("support.zendesk.com", query="API")

Cache

Responses are cached for 7 days in .cache/ (project-local) using hishel (SQLite-backed HTTP cache).

  • Use force_refresh=True on any tool to bypass the cache
  • Delete the .cache/ directory to clear all cached data
  • The .cache/ directory is gitignored

Override the default TTL (in seconds) with the ZENDESK_HC_CACHE_TTL environment variable in your .mcp.json:

{
  "mcpServers": {
    "zendesk-hc": {
      "command": "uv",
      "args": ["--directory", "/path/to/zendesk-hc-mcp", "run", "zendesk-hc-mcp"],
      "env": {
        "ZENDESK_HC_CACHE_TTL": "86400"
      }
    }
  }
}

Development

# Install dev dependencies
uv sync

# Run tests
uv run pytest -v

# Run the server (stdio mode)
uv run zendesk-hc-mcp

Contributing

Contributions are welcome! To get started:

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/my-feature)
  3. Install dev dependencies: uv sync
  4. Make your changes following TDD (write tests first)
  5. Run the test suite: uv run pytest -v
  6. Ensure coverage stays above 90%: uv run pytest --cov=zendesk_hc_mcp --cov-report=term-missing
  7. Submit a pull request

See CLAUDE.md for architecture details and development conventions.

Disclaimer

This project is not affiliated with, endorsed by, or sponsored by Zendesk, Inc. "Zendesk" is a registered trademark of Zendesk, Inc. This tool accesses the publicly available Zendesk Help Center API and is intended for lawful, read-only use.

License

MIT

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured