A
licenseA
qualityD
maintenanceMCP server for log file analysis. Gives LLMs the ability to efficiently analyze large log files without loading them into context.
7
101
MIT
pm4py-mcp
by azizketata
Related Servers
Alternatives to pm4py-mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityCmaintenanceLocal MCP server for Claude Code providing persistent memory, task planning, and agent coordination with full transparency and no network calls.2MIT
- AlicenseBqualityAmaintenanceLocal MCP server for specialist personas, reusable Markdown workflows, task-scoped subagent contracts and per-repository memory. Runs on your own computer with Python 3.11+ and a locally installed embedding model; its Claude Code plugin launches that separate installation.24814MIT
- AlicenseNot gradedqualityCmaintenanceMCP server that lets Claude Code delegate mechanical tasks to a local LLM for summarization, classification, extraction, and drafting.14 npm15MIT
- FlicenseAqualityDmaintenanceA Python MCP server that gives Claude access to PM workflow data such as sprints, roadmap, blockers, and workload, using synthetic fixture data (no API keys required) and can be forked for real Linear/Jira integration.7-
- AlicenseNot gradedqualityFmaintenanceA simple MCP server that enables Claude to communicate with locally running LLM models via LM Studio.9MIT
TDQS
A3.6/5.0
Scored across 67 tools
Disambiguation5/5
Each tool has a clearly distinct purpose, targeting different artifacts (logs, models, graphs, abstractions) and actions (discover, visualize, filter, abstract). Even similar tools like abstract_dfg vs discover_dfg+visualize_dfg are clearly separated by output type (text vs graph). Overlap is minimal and well-documented.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., discover_petri_net, abstract_log_features, filter_variants). There is no mixing of camelCase or other conventions, and the verbs are semantically grouped by function (discover_, abstract_, filter_, etc.).
Tool Count2/5
With 67 tools, the server is far above the typical 3-15 range. While the domain (process mining) is broad, many tools could be consolidated (e.g., multiple social network discovery tools into one with a parameter). This large surface likely overwhelms LLM agents and increases selection errors.
Completeness5/5
The tool set covers the full lifecycle of process mining: loading, describing, filtering, discovering (many model types), visualizing, abstracting, conformance checking, simulation, model conversion, and export. There are no significant gaps for the intended analytical workflow.
Maintenance
ActivityInactive
ResponsivenessNo issues