Microsoft GitHub Advisory MCP Server

Microsoft GitHub Advisory MCP Server

Enables querying GitHub Security Advisories from a local cloned advisory database using tools like list_advisories and get_advisory.

Category
Visit Server

README

Microsoft GitHub Advisory MCP Server

MCP server for querying GitHub Security Advisories from a local cloned advisory database.

Architecture

Two-Tier Design:

┌─────────────────────────────────────┐
│ MCP Server (stdio or HTTP)          │  Port: 18006 (HTTP mode)
│  - list_advisories tool             │
│  - get_advisory tool                │
└──────────┬──────────────────────────┘
           │ calls internally
┌──────────▼──────────────────────────┐
│ Local Express REST API              │  Port: 18005
│  GET /health                        │
│  GET /advisories                    │
│  GET /advisories/:ghsa_id           │
│  GET /search?q=<query>              │
└──────────┬──────────────────────────┘
           │ reads from
┌──────────▼──────────────────────────┐
│ LocalRepositoryDataSource           │
│  Reads JSON files from:             │
│  external/advisory-database/        │
└─────────────────────────────────────┘

Quick Start (VS Code + GitHub Copilot)

For users who just want to use the MCP server in VS Code:

  1. Clone and Setup:
git clone https://github.com/microsoft/github-advisory-mcp.git
cd github-advisory-mcp
npm install
npm run build
  1. The .vscode/mcp.json is pre-configured:
{
  "servers": {
    "advisory": {
      "command": "node",
      "args": ["${workspaceFolder}/dist/index.js"],
      "type": "stdio",
      "env": {
        "ADVISORY_REPO_PATH": "${workspaceFolder}/external/advisory-database"
      }
    }
  }
}
  1. Reload VS Code - Copilot will automatically:

    • Clone the advisory database (~310K advisories) on first use
    • Enable MCP tools: list_advisories, get_advisory
  2. Test in Copilot Chat:

@workspace Find high-severity npm advisories related to express

Done! The MCP server runs automatically when Copilot needs it.

Setup (Advanced)

1. Install Dependencies

npm install
npm run build

2. Database Setup (Optional - Auto-clones on first use)

# Linux/Mac (manual pre-clone)
./scripts/setup-advisory-database.sh

# Windows (manual pre-clone)
git clone --depth=1 https://github.com/github/advisory-database.git external/advisory-database

The database will auto-clone on first MCP tool call if not present.

Usage

Start Server (HTTP Streaming Mode)

Windows:

.\Start.ps1
# or with custom ports:
.\Start.ps1 -McpPort 18006 -ApiPort 18005

Manual Start:

$env:ADVISORY_REPO_PATH = "C:\path\to\advisory-database"
$env:MCP_PORT = "18006"
$env:ADVISORY_API_PORT = "18005"
node dist\http-server.js

Start Server (stdio Mode)

ADVISORY_REPO_PATH=/path/to/advisory-database node dist/index.js

Testing

Quick Test (Copilot Chat)

After setup, test in VS Code Copilot Chat:

@workspace /tests What tools does the advisory MCP server provide?

Or query advisories directly:

@workspace Find critical npm advisories from 2024
@workspace Get details for GHSA-jc85-fpwf-qm7x

Unit Tests (Automated)

npm test           # All tests
npm run test:e2e   # E2E tests (18 tests, ~9.5s after database cached)

Health Checks

# MCP Server Health
Invoke-RestMethod http://localhost:18006/health

# Local API Health
Invoke-RestMethod http://localhost:18005/health

Test Local REST API Directly

List advisories by ecosystem:

Invoke-RestMethod "http://localhost:18005/advisories?ecosystem=npm&per_page=5"

Get specific advisory:

Invoke-RestMethod "http://localhost:18005/advisories/GHSA-jc85-fpwf-qm7x"

Search advisories:

Invoke-RestMethod "http://localhost:18005/search?q=express"

Test MCP Tools

Initialize Session:

$body = @{
  jsonrpc = "2.0"
  id = 1
  method = "initialize"
  params = @{
    protocolVersion = "2024-11-05"
    capabilities = @{}
    clientInfo = @{ name = "test-client"; version = "1.0.0" }
  }
} | ConvertTo-Json -Depth 10

$response = Invoke-RestMethod -Uri "http://localhost:18006/mcp" -Method POST -Body $body -ContentType "application/json"
$sessionId = $response.result.sessionId

List Tools:

$body = @{
  jsonrpc = "2.0"
  id = 2
  method = "tools/list"
} | ConvertTo-Json

Invoke-RestMethod -Uri "http://localhost:18006/mcp" -Method POST -Body $body -ContentType "application/json" -Headers @{"Mcp-Session-Id"=$sessionId}

Call list_advisories:

$body = @{
  jsonrpc = "2.0"
  id = 3
  method = "tools/call"
  params = @{
    name = "list_advisories"
    arguments = @{
      ecosystem = "npm"
      severity = "high"
      per_page = 5
    }
  }
} | ConvertTo-Json -Depth 10

Invoke-RestMethod -Uri "http://localhost:18006/mcp" -Method POST -Body $body -ContentType "application/json" -Headers @{"Mcp-Session-Id"=$sessionId}

Environment Variables

Variable Default Description
ADVISORY_REPO_PATH ./external/advisory-database Path to cloned advisory-database repository
MCP_PORT 18006 Port for MCP HTTP server
ADVISORY_API_PORT 18005 Port for local REST API
ADVISORY_API_HOST 127.0.0.1 Host for local REST API
ADVISORY_API_BASE http://localhost:18005 Base URL for MCP tools to call local API

MCP Tools

list_advisories

List security advisories with optional filters.

Parameters:

  • ghsa_id (string): GHSA identifier
  • cve_id (string): CVE identifier
  • ecosystem (enum): Package ecosystem (npm, pip, maven, etc.)
  • severity (enum): Severity level (low, medium, high, critical, unknown)
  • cwes (string): Comma-separated CWE identifiers
  • is_withdrawn (boolean): Filter withdrawn advisories
  • affects (string): Package name filter
  • published (string): Published date filter
  • updated (string): Updated date filter
  • per_page (number): Results per page (max 100)
  • direction (enum): Sort direction (asc, desc)
  • sort (enum): Sort field (updated, published)

Example:

{
  "ecosystem": "npm",
  "severity": "critical",
  "per_page": 10
}

get_advisory

Get detailed information about a specific advisory.

Parameters:

  • ghsa_id (string, required): GHSA identifier (e.g., GHSA-xxxx-xxxx-xxxx)

Example:

{
  "ghsa_id": "GHSA-jc85-fpwf-qm7x"
}

Security & Validation

Input Validation

All MCP tool parameters are validated using Zod schemas:

list_advisories validation:

  • ecosystem: Enum of valid ecosystems (npm, pip, maven, etc.) - prevents injection
  • severity: Enum (low, medium, high, critical, unknown) - prevents injection
  • per_page: Number constrained to 1-100 max - prevents resource exhaustion
  • cwes: String pattern matching for CWE identifiers - validates format
  • Date filters: ISO 8601 format validation

get_advisory validation:

  • ghsa_id: Pattern match GHSA-xxxx-xxxx-xxxx - prevents path traversal
  • Required field enforcement - rejects empty/null values

File System Protection:

  • Path validation: Script execution paths validated against known safe scripts
  • No user-supplied paths accepted - prevents directory traversal
  • Read-only database access - no write operations exposed

Rate Limiting

Current: No rate limiting (stdio transport = single local user)

Considerations:

  • stdio mode: Single-process, single-user - no rate limiting needed
  • HTTP mode: Consider adding rate limiting if exposed beyond localhost
  • Database queries: Inherently rate-limited by disk I/O (310K+ files)

Future (HTTP mode):

// Example: express-rate-limit for HTTP endpoints
import rateLimit from 'express-rate-limit';

const limiter = rateLimit({
  windowMs: 15 * 60 * 1000, // 15 minutes
  max: 100 // limit each IP to 100 requests per windowMs
});

app.use('/mcp', limiter);

Session Security

  • Timeout: 30-minute automatic session cleanup
  • Isolation: Each MCP session isolated (no shared state)
  • Transport: stdio (local) or HTTP (localhost-only by default)

Known Security Measures

✅ Path validation for script execution (prevents command injection)
✅ Zod schema validation (prevents type confusion attacks)
✅ Session timeout (prevents resource leaks)
✅ Read-only database access (no write exposure)
✅ Enum validation for critical parameters (prevents injection)
⚠️ No rate limiting (stdio mode = trusted local user)
⚠️ No authentication (stdio mode = local process trust model)

Integration with Orchestrator

The MCP Advisory server can be integrated with orchestration platforms:

from mcp import ClientSession
from mcp.client.stdio import stdio_client

# Connect to MCP Advisory server
async with stdio_client(
    command="node",
    args=["dist/index.js"],
    env={
        "ADVISORY_REPO_PATH": "/path/to/advisory-database"
    }
) as (read, write):
    async with ClientSession(read, write) as session:
        # List npm advisories
        result = await session.call_tool(
            "list_advisories",
            arguments={"ecosystem": "npm", "per_page": 10}
        )

Port Allocation

  • 18005: Local REST API (Express server)
  • 18006: MCP HTTP streaming endpoint

Avoids conflicts with:

  • 18004: MCP Coder
  • 3333: Inspector MCP
  • 3500: Memory Service

Troubleshooting

Database Clone Failures

Issue: git clone fails for advisory-database

fatal: unable to access 'https://github.com/github/advisory-database.git/': Could not resolve host

Solutions:

  1. Check network connectivity: ping github.com
  2. Use VPN if behind corporate firewall
  3. Pre-download database: git clone --depth=1 https://github.com/github/advisory-database.git external/advisory-database
  4. Point to existing database: export ADVISORY_REPO_PATH=/path/to/existing/advisory-database

Timing: Initial clone takes 2-5 minutes (310K+ files, ~500MB)

Server Won't Start

Issue: Error: EADDRINUSE: address already in use

Solution:

# Check what's using the port
lsof -i :18005  # Local API
lsof -i :18006  # MCP server

# Kill process
kill -9 <PID>

Windows-Specific Issues

Issue: Bash script fails on Windows

./scripts/setup-advisory-database.sh
# bash: ./scripts/setup-advisory-database.sh: No such file or directory

Solution: Use Git Bash or WSL, or manual clone:

git clone --depth=1 https://github.com/github/advisory-database.git external/advisory-database

MCP Tools Not Available in Copilot

Issue: Copilot doesn't see advisory MCP tools

Solution:

  1. Verify .vscode/mcp.json exists in workspace root
  2. Reload VS Code: Ctrl+Shift+P → "Developer: Reload Window"
  3. Check build: npm run build and verify dist/index.js exists
  4. Check Copilot logs: Ctrl+Shift+P → "Developer: Open Logs Folder"

Query Performance Issues

Symptom: Slow advisory queries (>5 seconds)

Solutions:

  1. First query always slower (loads database index into memory)
  2. Use per_page parameter to limit results: per_page: 10
  3. Filter by ecosystem to reduce search space: ecosystem: "npm"
  4. Check disk I/O: Advisory database is 310K+ files

Performance Benchmarks:

  • First query (cold start): 2-4 seconds (index load)
  • Subsequent queries: 50-200ms (cached)
  • Database size: ~500MB, 310,635 files

Database Update Strategy

Automated Updates: The advisory database auto-updates on server start via git pull in setup script.

Manual Update:

cd external/advisory-database
git pull origin main

Update Frequency: GitHub updates advisory-database multiple times daily

Known Issues

Windows

  • The bash script setup-advisory-database.sh doesn't work on Windows
  • Workaround: Manually clone database or use existing one via ADVISORY_REPO_PATH
  • Use Start.ps1 script for convenient startup

Database Size

  • The advisory-database is ~100K+ JSON files
  • Shallow clone (--depth=1) recommended
  • First query loads entire index into memory (lazy loading)
  • Subsequent queries are fast (cached)

Development

CI/CD Pipeline

GitHub Actions Workflows:

  1. Build Validation (.github/workflows/build.yml)

    • Triggers: Push to main/dev, PRs
    • Matrix: Node.js 18.x, 20.x on Ubuntu latest
    • Steps: Checkout → Setup Node → npm ci → Build → Verify artifacts
    • Timing: ~27-33 seconds
  2. Copilot PR Review (.github/workflows/copilot-review.yml)

    • Triggers: PR opened or synchronized
    • Action: Automatically requests Copilot code review
    • Permissions: pull-requests: write, contents: read
    • Benefit: Automated AI code review on every PR

Timing Estimates:

  • npm ci: ~10 seconds (dependency install)
  • npm run build: ~4 seconds (TypeScript compilation)
  • Total CI time: ~27-33 seconds

Note: Tests are not run in CI (yet) because:

  • Database clone takes 2-5 minutes (310K+ files)
  • Would increase CI time to ~6-7 minutes per run
  • Consider separate "full test" workflow for main branch only

Watch Mode:

npm run dev

Clean Build:

npm run clean
npm run build

File Structure:

services/mcp/advisory/
├── src/
│   ├── index.ts              # stdio entry point
│   ├── http-server.ts        # HTTP streaming server
│   ├── server.ts             # MCP server factory
│   ├── local-server.ts       # Express REST API
│   ├── datasources/
│   │   └── local-repository.ts  # LocalRepositoryDataSource
│   ├── tools/
│   │   └── advisories.ts     # MCP tools (list/get)
│   └── types/
│       └── data-source.ts    # TypeScript interfaces
├── dist/                     # Compiled JavaScript
├── external/
│   └── advisory-database/    # Git submodule (cloned)
├── scripts/
│   └── setup-advisory-database.sh
├── package.json
├── tsconfig.json
├── Start.ps1                 # Windows startup script
└── README.md

Testing Status

Working:

  • TypeScript compilation
  • HTTP server startup (port 18006)
  • Local REST API (port 18005)
  • Health endpoints
  • Advisory listing with filters
  • Getting specific advisory by GHSA ID
  • Ecosystem filtering (npm, pip, etc.)
  • Pagination

Not Yet Tested:

  • MCP tool calls (list_advisories, get_advisory)
  • Session management
  • stdio mode
  • Multiple concurrent sessions
  • Search functionality

Contributing

This project does not accept external contributions (pull requests) at this time. Only verified committers from the Microsoft GitHub organization may contribute code.

If you would like to get involved:

See CONTRIBUTING.md for details.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

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
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

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
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