inspect-logs-mcp

MCP server for exploring inspect_ai evaluation logs from Claude Code.

What it does

This MCP server gives Claude Code, Cursor, and other LLMs direct access to your inspect_ai evaluation logs, allowing you to:

• List logs: See all your evaluation runs with metadata

• View summaries: Get detailed evaluation results, scores, and token usage

• Inspect samples: View full conversation history for any sample

• Search & filter: Find logs by task, model, date, or status

• Compare runs: Side-by-side comparison of two evaluation runs

• Aggregate stats: Get statistics across multiple runs

Related MCP server: Coval MCP Server

Installation

Currently only source installation is supported.

git clone https://github.com/PranshuSrivastava/inspect-logs-mcp.git
cd inspect-logs-mcp
pip install -e .

Configuration

To use with Claude code in your current directory, run the following command:

claude mcp add --transport stdio inspect-logs inspect-logs-mcp

To use with Claude code in your global configuration, run the following command:

claude mcp add --scope user --transport stdio inspect-logs inspect-logs-mcp

To use with Cursor, Antigravity or other IDEs, add the following to your mcp config file:

{
    "mcpServers": {
        "inspect-logs": {
            "command": "inspect-logs-mcp",
            "env": {
                "INSPECT_LOG_DIR": "./logs"
            }
        }
    }
}

Environment Variables

How it Works

The server reads .eval files (which are ZIP archives containing JSON) using the inspect_ai.log API. All file operations happen in memory - no files are extracted to disk, so your logs directory stays clean.

Requirements

• Python 3.10+

• inspect-ai >= 0.3.70

• mcp >= 1.0.0