{"server":{"$schema":"https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json","name":"io.github.hu-chenyu/log-ai-compressor","description":"Local log forensics: compress logs into citable evidence with root-cause confidence tiers.","title":"log-ai-compressor","repository":{"url":"https://github.com/hu-chenyu/log-ai-compressor","source":"github"},"version":"2.12.0","websiteUrl":"https://github.com/hu-chenyu/log-ai-compressor","packages":[{"registryType":"pypi","registryBaseUrl":"https://pypi.org","identifier":"log-ai-compressor","version":"2.12.0","runtimeHint":"uvx","transport":{"type":"stdio"},"runtimeArguments":[{"value":"log-ai-compressor[mcp]","type":"named","name":"--from"},{"value":"log-ai-compressor-mcp","type":"positional","valueHint":"command"}]}],"_meta":{"io.modelcontextprotocol.registry/publisher-provided":{"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"},"privacy":{"dataEgress":"none","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."},"tool":"mcp-publisher","toolVersion":"1.8.1","tools":[{"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_file"},{"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":"analyze_log_text"},{"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":"compare_log_files"},{"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":"export_report"},{"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":"get_cluster_detail"},{"description":"List built-in log parsing rule templates and the log formats each targets. Check this first when a log parses badly.","name":"list_rules"},{"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":"verify_evidence"},{"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.","name":"check_environment"}]}}},"_meta":{"io.modelcontextprotocol.registry/official":{"status":"active","statusChangedAt":"2026-10-19T04:50:01.565725Z","publishedAt":"2026-10-19T04:50:01.565725Z","updatedAt":"2026-10-19T04:50:01.565725Z","isLatest":false}}}