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Give your AI assistant access to real Helm chart data. No more hallucinated values.yaml files.
Give your AI assistant access to real Helm chart data. No more hallucinated values.yaml files.
Remote endpoints: streamable-http: https://helm-mcp.kubedoll.com/mcp
Valid MCP server (8 strong, 1 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry.
5 tools verified · Open access · No issues found
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Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"io-github-kubedoll-heavy-industries-helm-mcp": {
"url": "https://helm-mcp.kubedoll.com/mcp"
}
}
}From the project's GitHub README.
Give your AI assistant access to real Helm chart data. No more hallucinated values.yaml files.
When you ask Claude, Cursor, or other AI assistants to help with Kubernetes deployments, they don't have access to Helm chart schemas. So they guess — and the guesses look plausible but don't match reality.
Without mcp-helm:
With mcp-helm:
mcp-helm implements the Model Context Protocol (MCP) — a standard way for AI assistants to access external data sources.
Add this to your editor's MCP config to use our public instance (rate limited, no install required):
{
"mcpServers": {
"helm": {
"type": "http",
"url": "https://helm-mcp.kubedoll.com/mcp"
}
}
}
Then ask your AI: "What values can I configure for the bitnami/postgresql chart?"
Edit ~/.claude/mcp.json:
{
"mcpServers": {
"helm": {
"command": "docker",
"args": ["run", "--rm", "-i", "ghcr.io/kubedoll-heavy-industries/mcp-helm", "--transport=stdio"]
}
}
}
Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"helm": {
"command": "docker",
"args": ["run", "--rm", "-i", "ghcr.io/kubedoll-heavy-industries/mcp-helm", "--transport=stdio"]
}
}
}
Edit MCP settings in Cursor's configuration:
{
"mcpServers": {
"helm": {
"command": "docker",
"args": ["run", "--rm", "-i", "ghcr.io/kubedoll-heavy-industries/mcp-helm", "--transport=stdio"]
}
}
}
Add to your Continue config (~/.continue/config.json):
{
"experimental": {
"modelContextProtocolServers": [
{
"transport": {
"type": "stdio",
"command": "docker",
"args": ["run", "--rm", "-i", "ghcr.io/kubedoll-heavy-industries/mcp-helm", "--transport=stdio"]
}
}
]
}
}
If you prefer to run the binary directly, install mcp-helm and replace the Docker config with:
{
"mcpServers": {
"helm": {
"command": "mcp-helm"
}
}
}
| Tool | What it does |
|---|---|
search_charts | Search for charts in a Helm repository |
get_versions | Get available versions of a chart (newest first, use limit=1 for latest) |
get_values | Get chart values.yaml with optional JSON schema (include_schema=true) |
get_dependencies | Get chart dependencies from Chart.yaml |
get_notes | Get chart NOTES.txt (post-install instructions) |
Docker (recommended — no install required, used in Editor Setup above):
docker pull ghcr.io/kubedoll-heavy-industries/mcp-helm:latest
Binary:
curl -fsSL https://github.com/Kubedoll-Heavy-Industries/helm-mcp/releases/latest/download/mcp-helm_$(uname -s)_$(uname -m).tar.gz | tar xz
sudo mv mcp-helm /usr/local/bin/
Go:
go install github.com/Kubedoll-Heavy-Industries/helm-mcp/cmd/mcp-helm@latest
For shared deployments or when you need an HTTP endpoint:
docker run -p 8012:8012 ghcr.io/kubedoll-heavy-industries/mcp-helm:latest \
--transport=http --listen=:8012
# Connect to http://localhost:8012/mcp
See docs/self-hosting.md for health endpoints and production recommendations.
MIT — see LICENSE.
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