inspect-logs-mcp
Related Servers
Alternatives to inspect-logs-mcp
No user-submitted related servers found.
Related Servers
- AlicenseAqualityCmaintenanceEnables AI assistants to automatically inspect and analyze application runtime log files for debugging and troubleshooting. Supports monitoring multiple log directories simultaneously with tools for listing, reading, searching, and paginating through log files.818 PyPIMIT
- AlicenseNot gradedqualityAmaintenanceEnables AI agents to analyze, cluster, compare, and export reports from local log files through seven read-only tools, returning compact evidence packs with line-number citations instead of raw log dumps. All processing happens on the local machine, so log data never leaves the environment.526 PyPI1MIT
- AlicenseNot gradedqualityDmaintenanceProvides AI coding agents with tools to query, search, and analyze persisted HTTP request logs, enabling efficient debugging without terminal noise.MIT
- AlicenseAqualityBmaintenanceEnables AI agents such as Cursor and Claude Code to inspect local log files through MCP tools for tailing, searching, filtering, statistics, and root-cause diagnosis using local Ollama or a built-in rule engine.9MIT
- FlicenseNot gradedqualityAmaintenanceEnables AI agents to query and debug Langfuse traces, sessions, and exceptions locally, with route-decision tools, metrics, prompt and dataset access, plus compact or file-dump output for token control.-
- FlicenseNot gradedqualityAmaintenanceEnables AI agents and MCP clients to locally debug Langfuse data through first-class tools for traces, observations, sessions, exceptions, route decisions, prompts, datasets, annotation queues, scores, and metrics. Supports selective tool-group loading, compact or file-dump output, and a read-only mode for safer exploration from Claude Code, Codex, Cursor, or any MCP client.-
TDQS
Scored across 6 tools
Each tool has a clearly distinct purpose with no overlap: compare_runs compares two specific runs, get_aggregate_stats provides cross-run statistics, get_eval_summary gives detailed metadata for a single run, get_sample retrieves granular sample data, list_logs enumerates available logs, and search_logs filters logs by criteria. The descriptions clearly differentiate their scopes, making misselection unlikely.
All tools follow a consistent verb_noun pattern with 'tool_' prefix (e.g., tool_compare_runs, tool_get_aggregate_stats). The verbs are descriptive and appropriate for their functions (compare, get, list, search), and the nouns precisely indicate the target resources (runs, stats, summary, sample, logs). This consistency aids predictability and usability.
With 6 tools, this server is well-scoped for inspecting evaluation logs. The count aligns with the domain's needs: listing and searching logs, retrieving summaries and samples, comparing runs, and aggregating statistics. Each tool earns its place without redundancy, covering essential operations without being overwhelming or insufficient.
The toolset provides complete coverage for log inspection: list_logs and search_logs for discovery, get_eval_summary and get_sample for detailed analysis, compare_runs for comparison, and get_aggregate_stats for cross-run insights. There are no obvious gaps; agents can navigate from discovery to deep analysis seamlessly, supporting typical evaluation workflows.