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MCP Log Reader

by klara-research
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MCP Server: Analyze & Debug MCP Logs

🎯 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

Related MCP server: Log Analyzer MCP

🚀 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:

Parameter

Description

Default

lines

Number of lines to read from each log file

100

filter

Text to filter log entries by (case-insensitive)

""

customPath

Custom path to log directory

OS-specific

fileLimit

Maximum number of files to read per page

5

page

Page number for pagination

1

💡 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

Available Tools

1 tool
read_mcp_logsC

Read MCP logs from the standard location

ParametersJSON Schema
NameRequiredDescriptionDefault
customPathNoOptional custom path to log directory (default is system-specific)
fileLimitNoMaximum number of files to read per page (default: 5)
filterNoOptional text to filter log entries by (case-insensitive)
linesNoNumber of lines to read from the end of each log file (default: 100)
pageNoPage number for pagination (default: 1)

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions reading logs but fails to describe key behaviors like whether this is a safe read operation, what the output format looks like, or any limitations (e.g., file size constraints, error handling). This leaves significant gaps for a tool with multiple parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 5 parameters, no annotations, and no output schema, the description is incomplete. It does not address what the tool returns, how results are structured, or behavioral aspects like pagination details or error conditions, which are crucial for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, meaning all parameters are documented in the input schema. The description does not add any additional meaning or context beyond what the schema provides, such as explaining interactions between parameters or typical use cases. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Read') and resource ('MCP logs from the standard location'), providing a specific verb+resource combination. However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, so it falls short of a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description offers no guidance on when to use this tool versus alternatives, prerequisites, or exclusions. It simply states what the tool does without context for its application, leaving the agent to infer usage scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.1/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools, making disambiguation perfect. The tool's purpose is clearly defined as reading MCP logs from a standard location, leaving no room for confusion with other tools.

Naming Consistency5/5

The single tool name 'read_mcp_logs' follows a clear verb_noun pattern, which is inherently consistent as there are no other tools to compare it against. This naming convention is straightforward and predictable for any potential future tools.

Tool Count2/5

A single tool is too few for a server named 'MCP Log Reader', as it suggests a limited scope that may not cover common log-related operations like filtering, searching, or managing logs. This minimal count feels thin and inadequate for the apparent domain of log reading.

Completeness2/5

The tool surface is severely incomplete for log reading; it only provides a basic read function without essential operations such as filtering by date, searching for specific entries, or handling different log levels. This gap will likely cause agent failures when more complex log interactions are needed.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

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