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MCP Server: Analyze & Debug MCP Logs

MCP Server: Analyze & Debug MCP Logs

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github.com/klara-researchDeveloper Tools16Updated 78w ago
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About MCP Server: Analyze & Debug MCP Logs

MCP Log Reader helps Claude inspect and troubleshoot MCP logs. It locates platform-specific log directories and offers filtering and pagination for large collections.

Key features

  1. 01Locate logs across platforms
  2. 02Filter text without case sensitivity
  3. 03Browse log files with pagination
  4. 04Limit processing of large files

Use cases

  1. 01Find MCP connection errors
  2. 02Investigate initialization problems
  3. 03Browse large log collections

MCP support

The MCP server gives clients such as Claude access to log-reading and analysis capabilities, returning filtered and paginated log content.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "MCP-Analyzer": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli"
      ]
    }
  }
}
Run commandnpx -y @smithery/cli

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@smithery/cli

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

MCP Server: Analyze & Debug MCP Logs

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README image


🔍 Read logs from standard locations across all platforms

🔎 Filter, paginate, and analyze large log collections

🎯 Overview

MCP Log Reader is a specialized MCP server that helps you analyze and debug Model Context Protocol logs. It provides Claude with direct access to log files, making it easy to troubleshoot MCP integrations and understand how Claude interacts with your tools.

  • Multi-platform Support: Works on macOS, Windows, and Linux with platform-specific log paths
  • Smart Filtering: Find specific log entries with case-insensitive text search
  • Paginated Browsing: Navigate large log collections efficiently
  • Size Management: Handles large log files with intelligent truncation
  • Seamless Claude Integration: Works directly with Claude Desktop

🚀 Quick Start

Installing via Smithery

To install MCP Log Reader for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @klara-research/MCP-Analyzer --client claude
Installing Manually

Install directly from GitHub:

# Clone the repository
git clone https://github.com/klara-research/MCP-Analyzer.git
cd MCP-Analyzer

# Install dependencies
npm i

Build and run:

# Compile TypeScript
npx tsc

🔌 Connecting to Claude

Add the server to your Claude Desktop configuration:

{
  "mcpServers": {
    "log-reader": {
      "command": "node",
      "args": [
        "/absolute/path/MCP-Analyzer/build"
      ]
    }
  }
}

Then restart Claude Desktop.

📋 Available Parameters

The log reader supports these parameters:

ParameterDescriptionDefault
linesNumber of lines to read from each log file100
filterText to filter log entries by (case-insensitive)""
customPathCustom path to log directoryOS-specific
fileLimitMaximum number of files to read per page5
pagePage number for pagination1

💡 Example Usage

Ask Claude to use the log reader tool:

Can you check my MCP logs for any connection errors in the last day?

Or with specific parameters:

Can you look through MCP logs with filter="error" and lines=50 to find initialization issues?

⚙️ How It Works

  1. The server automatically detects your OS and finds the appropriate log directory
  2. It locates all MCP log files and sorts them by modification time (newest first)
  3. The requested page of log files is retrieved based on pagination settings
  4. Files are processed with size limits to prevent overwhelming responses
  5. Filtered content is returned in a structured format with pagination details

📄 License

MIT License

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.