gha-intel-mcp
MCP server that analyzes GitHub Actions workflow performance, audits configuration for optimization, and provides billing and cache usage insights.
README
<img src="./assets/banner-gha-intel.svg" alt="gha-intel-mcp" width="888" />
An MCP server for GitHub Actions workflow timing analysis, configuration auditing, and billing insights.
Tools
| Tool | Description |
|---|---|
list_workflow_performance |
Computes average, min, max, and p95 duration statistics for recent workflow runs. |
analyze_workflow_config |
Evaluates workflow YAML for caching, parallelism, concurrency, artifacts, checkout depth, timeouts, runner pinning, Docker caching, and triggers. |
get_billing_usage |
Returns Actions billing minutes and estimated cost by runner type, plus per-repo cache utilisation. |
Requirements
- Node.js >= 18 (uses native
fetch) - A GitHub personal access token with
repoandread:orgscopes
Setup
Three transport modes are available. Choose whichever fits your deployment:
Option A: stdio (local, recommended for desktop clients)
The server runs as a subprocess of the MCP client over stdin/stdout. No network port required.
Claude Desktop
~/Library/Application Support/Claude/claude_desktop_config.json (macOS)
%APPDATA%\Claude\claude_desktop_config.json (Windows)
{
"mcpServers": {
"gha-intel": {
"command": "npx",
"args": ["-y", "@barissozudogru/gha-intel-mcp"],
"env": {
"GITHUB_TOKEN": "ghp_your_token"
}
}
}
}
Claude Code
claude mcp add gha-intel -e GITHUB_TOKEN=ghp_your_token -- npx -y @barissozudogru/gha-intel-mcp
Cursor
~/.cursor/mcp.json
{
"mcpServers": {
"gha-intel": {
"command": "npx",
"args": ["-y", "@barissozudogru/gha-intel-mcp"],
"env": {
"GITHUB_TOKEN": "ghp_your_token"
}
}
}
}
Windsurf
~/.codeium/windsurf/mcp_config.json
{
"mcpServers": {
"gha-intel": {
"command": "npx",
"args": ["-y", "@barissozudogru/gha-intel-mcp"],
"env": {
"GITHUB_TOKEN": "ghp_your_token"
}
}
}
}
VS Code + Copilot
.vscode/mcp.json (workspace) or user settings
{
"servers": {
"gha-intel": {
"type": "stdio",
"command": "npx",
"args": ["-y", "@barissozudogru/gha-intel-mcp"],
"env": {
"GITHUB_TOKEN": "ghp_your_token"
}
}
}
}
Cline
Open Cline settings, navigate to MCP Servers, and add:
{
"mcpServers": {
"gha-intel": {
"command": "npx",
"args": ["-y", "@barissozudogru/gha-intel-mcp"],
"env": {
"GITHUB_TOKEN": "ghp_your_token"
}
}
}
}
Continue.dev
~/.continue/config.yaml
mcpServers:
- name: gha-intel
command: npx
args:
- -y
- "@barissozudogru/gha-intel-mcp"
env:
GITHUB_TOKEN: ghp_your_token
Zed
~/.config/zed/settings.json
{
"context_servers": {
"gha-intel": {
"command": {
"path": "npx",
"args": ["-y", "@barissozudogru/gha-intel-mcp"],
"env": {
"GITHUB_TOKEN": "ghp_your_token"
}
}
}
}
}
JetBrains (IntelliJ, PyCharm, WebStorm, etc.)
Go to Settings > Tools > AI Assistant > MCP and add:
{
"mcpServers": {
"gha-intel": {
"command": "npx",
"args": ["-y", "@barissozudogru/gha-intel-mcp"],
"env": {
"GITHUB_TOKEN": "ghp_your_token"
}
}
}
}
Option B: HTTP (remote or cloud clients)
Start the server in HTTP mode and point clients at the endpoint:
GITHUB_TOKEN=ghp_your_token npx @barissozudogru/gha-intel-mcp --http
# Server listens on http://0.0.0.0:3000/mcp
# Health check: http://localhost:3000/health
Or set via environment variable instead of the flag:
TRANSPORT=http PORT=3000 GITHUB_TOKEN=ghp_your_token npx @barissozudogru/gha-intel-mcp
Cursor (HTTP)
~/.cursor/mcp.json
{
"mcpServers": {
"gha-intel": {
"url": "http://localhost:3000/mcp"
}
}
}
VS Code + Copilot (HTTP)
.vscode/mcp.json
{
"servers": {
"gha-intel": {
"type": "http",
"url": "http://localhost:3000/mcp"
}
}
}
Windsurf (HTTP)
~/.codeium/windsurf/mcp_config.json
{
"mcpServers": {
"gha-intel": {
"serverUrl": "http://localhost:3000/mcp"
}
}
}
Continue.dev (HTTP)
~/.continue/config.yaml
mcpServers:
- name: gha-intel
url: http://localhost:3000/mcp
Option C: Docker
docker build -t gha-intel-mcp .
docker run -p 3000:3000 -e GITHUB_TOKEN=ghp_your_token gha-intel-mcp
The container starts in HTTP mode by default. Point your client at http://localhost:3000/mcp.
Tool Reference
list_workflow_performance
Fetch real run timing data and compute job-level statistics.
| Parameter | Type | Required | Description |
|---|---|---|---|
owner |
string | yes | GitHub owner (user or org) |
repo |
string | yes | Repository name |
workflow_id |
string | yes | Workflow file name (e.g. ci.yml) or numeric ID |
count |
number | no | Number of recent runs to analyse (default: 10, max: 100) |
Output: Per-job and per-step timing stats (avg, min, max, p95), overall run timing, and a list of recent run conclusions.
analyze_workflow_config
Parse and audit a workflow YAML for optimisation opportunities.
| Parameter | Type | Required | Description |
|---|---|---|---|
workflow_content |
string | yes | Full YAML content of the workflow file |
Output: Findings grouped by severity (critical / warning / info / good) across nine categories, each with a concrete recommendation.
Categories analysed: Dependency caching, matrix strategy and fail-fast, concurrency groups and cancel-in-progress, artifact uploads, git checkout depth, job timeout-minutes, runner version pinning, Docker layer caching, and trigger path filters.
get_billing_usage
Retrieve billing and cache consumption data.
| Parameter | Type | Required | Description |
|---|---|---|---|
owner |
string | yes | GitHub username or organisation |
repo |
string | no | Repository name for repo-scoped cache and run stats |
Output: Total minutes used, plan utilisation, estimated cost broken down by runner type (Ubuntu / macOS / Windows / large runners), plus per-repo cache size and utilisation percentage.
Environment Variables
| Variable | Required | Description |
|---|---|---|
GITHUB_TOKEN |
yes | GitHub personal access token. Requires repo scope for private repos, read:org for org billing. |
TRANSPORT |
no | Set to http to enable HTTP mode (default: stdio). |
PORT |
no | HTTP port when running in HTTP mode (default: 3000). |
License
MIT
Recommended Servers
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.
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.
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.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
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.
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.
Neon Database
MCP server for interacting with Neon Management API and databases
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.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
E2B
Using MCP to run code via e2b.