Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@pmcontrols-mcp What is the critical path for activities: A(5d), B(3d), C(2d) with A before C and B before C?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP server that exposes pmcontrols, the validated project scheduling and earned value library for Python, as tools for AI agents: from critical-path and earned-value analysis to ready-to-show charts (Gantt, network, S-curve, criticality, completion histogram).
Agents asked to plan a project or report its status tend to generate the arithmetic themselves: a backward pass done by eye, an earned-value index inverted, an earned schedule mistaken for schedule variance. Generated project metrics fail silently. The calculation belongs in a deterministic, versioned, validated library that the agent calls, which leaves the agent to choose the analysis and explain the result.
Analysis tools return the library's structured payload: named statistics, a tidy table, structured alerts, and provenance (library version, input hash, timestamp).
Tool | Purpose |
| CPM forward and backward pass: ES, EF, LS, LF, slack, critical path |
| PERT three-point analysis with a Monte Carlo completion distribution and criticality indices |
| minimum-cost schedule compression to a deadline, solved as a linear program |
| the full EVM indicator set with Lipke earned schedule, against a planned-value baseline |
| the earned schedule for a given earned value |
Chart tools return a PNG image the client can display.
Tool | Purpose |
| a Gantt chart of the schedule, critical path highlighted |
| the activity network with the critical path |
| the earned value S-curve (PV/EV/AC + forecast) |
| Monte Carlo per-activity criticality bars |
| Monte Carlo completion-time histogram |
Related MCP server: NYC Open Data Capital Projects MCP Server
pip install pmcontrols-mcpOr run it without installing, with uv:
uvx pmcontrols-mcpAdd the server to your MCP client's configuration:
{
"mcpServers": {
"pmcontrols": {
"command": "pmcontrols-mcp"
}
}
}The server communicates over stdio and works with any MCP-compatible client.
Calling critical_path with a list of activities returns a structured
result the agent reads directly, instead of computing the schedule itself:
{
"method": "cpm",
"stats": {"project_duration": 15.0, "n_activities": 8.0, "n_critical": 5.0},
"meta": {
"critical_activities": ["A", "C", "E", "G", "H"],
"version": "0.2.1",
"input_hash": "sha256:...",
"computed_at": "2026-06-15T09:14:02+00:00"
},
"table": {"activity": ["A", "B", "..."], "slack": [0.0, 1.0, "..."]}
}Every result carries provenance (library version, input hash, timestamp), so a figure an agent reports can be recomputed and audited later.
The reasoning behind routing project-control arithmetic through a validated tool, rather than letting a model generate it, is set out in Project control is not a language task.
pmcontrols is the underlying library this server wraps.
MIT. Written and maintained by Atakan Arikan, MSc Student at Tsinghua University and Politecnico di Milano.
This server cannot be deployed
AI-powered project controls tools for Primavera P6 and Primavera Unifier.
Forensic scheduling MCP for Primavera P6 (XER): AACE windows, DCMA-14, Monte Carlo, TIA.
Preflight QA for AI-agent deliverables with structured verdicts and repair guidance.
AI-native project management + agent memory: tasks, sprints, risk, burnout, knowledge search.
