🚀 Log Reader Mcp

🚀 Stop wasting time copy-pasting logs! 🧠 Let Cursor's AI instantly access, search, and explain your logs — no more manual work, just answers.

📚 Table of Contents

• Why Log Reader Mcp?

• Installation

  • Automatic (recommended)

  • Manual

• Who is it for?

• MCP Configuration

• Example Prompts for Cursor

• CLI Usage

• Log Format (JSON per line)

• Developer Guide

• Key Advantages

• FAQ

• Getting Help

• Contributing

• License

• Cursor Rule (Workflow)

✨ Why Log Reader Mcp?

• 🤖 AI-powered log access: Give your AI assistant (Cursor, etc.) direct, on-demand access to your app logs.

• 🧠 Smarter debugging: Let the AI analyze, summarize, and explain logs as you code.

• ⏱️ Save hours: No more switching terminals, tailing files, or hunting for errors—get instant feedback and context.

• 🛡️ Safe & isolated: Never pollutes your project, robust CLI and test coverage.

• ⚡ Plug & Play: One command, zero config, works everywhere.

👤 Who is it for?

• Backend & frontend developers

• DevOps & SREs

• Teams using AI-powered editors (Cursor, etc.)

• Anyone who wants faster, smarter log analysis!

📦 Installation

🚀 Automatic (recommended)

• Installs everything, creates .cursor/mcp.json and workflow rules, and sets up your logs folder automatically.

🛠️ Manual

1. Install the package

   npm install --save-dev log-reader-mcp

2. Create the config file

   • At the root of your project, create a folder named .cursor (if it doesn't exist).

   • Inside .cursor/, create a file named mcp.json with:

   { "mcpServers": { "log-reader-mcp": { "command": "npx", "args": ["-y", "log-reader-mcp"] } }, "mcp.enabled": true, "mcp.autoStart": true, "mcp.showStatusBar": true, "mcp.logLevel": "info" }

   • This tells your editor (Cursor, VSCode, etc.) how to launch and connect to the log reader mcp server for your project.

🖼️ What does it do?

Log Reader Mcp exposes your application's logs to your AI assistant/editor (like Cursor) via the Model Control Protocol (MCP). This means:

• The AI can read, filter, and analyze your logs on demand (not streaming)

• You can ask the AI to fetch logs for a specific period, number of lines, error level, etc.

• Makes onboarding, debugging, and incident response dramatically faster

🔧 Key Features

• Simplified Interface: No logPath parameter needed - always uses logs/logs.log in your working directory

• Automatic Detection: The server automatically finds and reads your log file

• Time-based Filtering: Filter logs by specific time ranges using ISO 8601 format

• Line-based Reading: Read the last N lines with automatic validation

• Structured JSON: Full support for structured logging with metadata

💡 Example Prompts for Cursor

Here are some real-world prompts you can use in Cursor (or any MCP-enabled AI) to interact with your logs:

Note: The tool automatically uses logs/logs.log in your current working directory. The logPath parameter has been removed for maximum simplicity - no need to specify any file path!

Tip: You can combine filters, time ranges, and keywords in your prompts. The AI will use Log Reader Mcp to fetch and analyze the relevant log data for you!

💡 Use Cases

⚙️ MCP Configuration Example

• 📁 Place this in .cursor/mcp.json

• Your editor will auto-detect and use the log server

🖥️ CLI Usage

📝 Log Format (JSON per line)

Each line in logs/logs.log should be a JSON object:

🧑‍💻 Developer Guide

• Release & Versioning: Automated with semantic-release, changelog, and version auto-sync

• CI/CD: GitHub Actions (.github/workflows/)

• Testing: 100% coverage, CLI test isolation, robust integration

• Project Structure:

  • src/ — TypeScript sources

  • bin/cli.js — CLI entry point

  • templates/ — MCP config & workflow templates

  • .github/workflows/ — CI/CD

🏆 Key Advantages

• 🔒 Zero config, zero risk: Never pollutes your project

• 🧪 100% tested: Full test isolation, robust CI

• 🏗️ AI-ready: Structured logs, perfect for automated analysis

• 🚀 Plug & Play: Works with all MCP editors, no setup required

• ⏳ Massive time savings: Focus on code, not on chasing logs

🤝 Contributing

1. Fork & create a branch

2. Use conventional commits

3. npm run build to compile

4. npm test to verify

5. Open a clear, detailed PR

📄 License

MIT

📝 Cursor Rule (Workflow)

To help Cursor (or any MCP-compatible AI) understand your log structure and best practices, you can add a workflow rule file:

How to add the Cursor rule

1. Copy the template

   • Use the command: npx log-reader-mcp init (recommended)

   • Or manually copy templates/mcp-log-server/workflow.mdc to .cursor/log-reader-mcp/workflow.mdc at the root of your project.

2. What does this rule do?

   • It describes the log file location, format, and usage standards for your project.

   • It helps the AI agent (Cursor, etc.) understand how to read, filter, and analyze your logs.

   • It documents best practices for logging, security, and debugging for your team.

Example (excerpt)

Why add this rule?

• 🧠 For the AI: It enables Cursor to provide smarter, context-aware log analysis and suggestions.

• 👩‍💻 For developers: It ensures everyone follows the same standards and makes onboarding easier.

• 🔒 For security: It reminds everyone not to log sensitive data and to validate log structure.

Tip: Keeping this rule up to date helps both humans and AI work better with your logs!

❓ FAQ

Q: Is it compatible with VSCode or only Cursor?
A: Any editor supporting MCP can use it, including Cursor and future tools.

Q: Can I use multiple MCP servers?
A: Yes, just add more entries in .cursor/mcp.json.

Q: What log formats are supported?
A: Only structured JSON logs (one object per line) are supported for full AI analysis.

Q: Is it safe for production?
A: Yes! The tool never modifies your logs, only reads them, and is fully tested.

💬 Getting Help

• Open an issue for bugs or questions

• Join the discussion on GitHub Discussions

• See the Cursor Rule Template for advanced configuration