PyP6Xer MCP Server
Related Servers
Alternatives to PyP6Xer MCP Server
No user-submitted related servers found.
Related Servers
- AlicenseAqualityAmaintenanceEnables an AI assistant to analyse, and when permitted edit, Primavera P6 Professional schedules on a Windows PC, whether they live in a standalone SQLite database or plain XER files. It provides critical path and float analysis, DCMA health checks, progress comparisons, and in-memory what-if scenarios, with write operations off by default and protected by dry runs, backups, and an edit log.115Apache 2.0
- AlicenseNot gradedqualityAmaintenanceEnables MCP-compatible AI clients to query, analyze, and mutate Oracle Primavera P6 schedules from XER files or live P6 EPPM, with tools for critical path, DCMA compliance, earned value, resources, baselines, and exports.35 PyPI1MIT
- AlicenseAqualityCmaintenanceEnables assistants to read and analyze construction schedules like a scheduler: CPM, float, critical paths, DCMA 14-point, logic checks, update-to-update comparison, baseline variance, and S-curves from P6 XER and MS Project files.17MIT
- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to control, automate, and query desktop project-management schedules in real time via native Windows COM, including task/WBS management, resource and cost assignment, baselines, critical path, status reporting, and PDF export.MIT
- AlicenseNot gradedqualityCmaintenanceEnables Claude and other AI agents to build, edit, and analyze Microsoft Project desktop schedules on Windows through natural language, covering tasks, WBS, dependencies, resources, calendars, baselines, critical path, resource leveling, and earned value, with live changes in Project and single-step undo. It also exports results to PDF or CSV and reads project health and time-phased data for S-curves and histograms.MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI clients to build and calculate critical-path project plans through tools, including resource leveling, vacation and holiday calendars, what-if scenario comparison and Monte Carlo schedule risk simulation. Projects are read from and written as plain JSON files.MIT
TDQS
Scored across 29 tools
Several tools overlap in purpose: schedule_health_check consolidates float/critical path/open-end data, generate_report bundles slipping activities/health/earned value, and work_package_summary overlaps with wbs_analysis. The descriptions help, but an agent could easily select the wrong analysis or report tool for a given request.
All tools use a consistent pyp6xer_ prefix and snake_case, with clear list/get/search/update/export verbs for operations. A minority of analysis tools use noun-style names such as critical_path, float_analysis, and earned_value, but the overall pattern remains readable and predictable.
At 29 tools, the surface is dense and exceeds the 25-tool threshold. Many narrow analysis/report variants could be consolidated without losing capability, and the large count increases the chance of an agent picking a redundant tool.
The domain is well covered: loading files, listing projects/calendars/resources/activities, searching, detailed activity views, WBS/earned value/float/critical path/quality analyses, snapshot comparison, and update/write/export workflows are all present. The main gap is that modification is limited to activity fields, with no create/delete or relationship/resource editing.