log-ai-compressor

{
  "name": "io.github.hu-chenyu/log-ai-compressor",
  "_meta": {
    "io.modelcontextprotocol.registry/publisher-provided": {
      "tool": "mcp-publisher",
      "tools": [
        {
          "name": "analyze_log_file",
          "description": "Analyze one or more local log files (merged oldest-first by mtime, suitable for rotated logs). Returns error clusters ranked by priority, each with a root-cause confidence tier (CONFIRMED / LIKELY / INSUFFICIENT) and an explicit list of what evidence is still missing. Runs fully on-device."
        },
        {
          "name": "analyze_log_text",
          "description": "Analyze a log text directly — pasted content, command output, or a fragment an agent already read. Same clustering and root-cause confidence output, without touching the filesystem."
        },
        {
          "name": "compare_log_files",
          "description": "Compare 2-5 log files and list errors that are new, disappeared, or common relative to a baseline file. Answers 'what changed between this deploy and the last one'."
        },
        {
          "name": "export_report",
          "description": "Export analysis as a compressed report in md / json / text / html / summary format. Markdown and summary are tuned for pasting into an LLM: a few hundred tokens instead of millions of raw log lines, with every finding still carrying its line numbers for citation."
        },
        {
          "name": "get_cluster_detail",
          "description": "Retrieve the full evidence for one error cluster: representative raw log line, surrounding context lines, de-noised stack trace, instance line-number index, and the reasoning behind its root-cause verdict."
        },
        {
          "name": "list_rules",
          "description": "List built-in log parsing rule templates and the log formats each targets. Check this first when a log parses badly."
        },
        {
          "name": "verify_evidence",
          "description": "Re-read the source log and re-verify every evidence reference in a report: file, line range, sha256 digest. Returns one of four verdicts - VERIFIED (every reference matches), TAMPERED (content really changed), UNVERIFIABLE (cannot check right now, e.g. the log is momentarily locked by another process - NOT an accusation of tampering), NO_EVIDENCE. This is the tool's trust anchor - the agent does not need to believe the analysis, it can re-run this step itself."
        },
        {
          "name": "check_environment",
          "description": "Self-check with no arguments: confirms the tool is importable and reports version, available built-in rules, and whether optional AI interpretation is configured."
        }
      ],
      "privacy": {
        "note": "All analysis runs on-device. The server only reads local file paths the calling agent passes in; no network calls are made unless the optional AI-interpretation layer is explicitly configured.",
        "dataEgress": "none"
      },
      "benchmark": {
        "note": "Measured on 10 Loghub-2.0 datasets (ISSTA'24) whose event templates are human-annotated. At an equal 32k-token budget, evidence retention vs Drain3 is 7 wins / 1 tie / 2 losses - the two losses (HPC, Mac) are documented with root causes rather than hidden. Output size stays bounded in the low hundreds of tokens regardless of log size (re-measured 313-788 tokens on 120k-180k line logs), whereas Drain3's output grows from 317 to 566,802 tokens. Full methodology and limitations: https://github.com/hu-chenyu/log-ai-compressor/blob/main/docs/BENCHMARK.md",
        "source": "https://zenodo.org/record/8275861"
      },
      "toolVersion": "1.8.1"
    }
  },
  "title": "log-ai-compressor",
  "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
  "version": "2.12.3",
  "packages": [
    {
      "version": "2.12.3",
      "transport": {
        "type": "stdio"
      },
      "identifier": "log-ai-compressor",
      "runtimeHint": "uvx",
      "registryType": "pypi",
      "registryBaseUrl": "https://pypi.org",
      "runtimeArguments": [
        {
          "name": "--from",
          "type": "named",
          "value": "log-ai-compressor[mcp]"
        },
        {
          "type": "positional",
          "value": "log-ai-compressor-mcp",
          "valueHint": "command"
        }
      ]
    }
  ],
  "repository": {
    "url": "https://github.com/hu-chenyu/log-ai-compressor",
    "source": "github"
  },
  "websiteUrl": "https://github.com/hu-chenyu/log-ai-compressor",
  "description": "Local log forensics: compress logs into citable evidence with root-cause confidence tiers."
}