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
100
MIT
pm4py-mcp
by azizketata
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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