pmcontrols-mcp
An MCP server that exposes pmcontrols, the validated project scheduling and earned value library for Python, as tools for AI agents.
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.
Tools
| Tool | Purpose |
|---|---|
critical_path |
CPM forward and backward pass: ES, EF, LS, LF, slack, critical path |
schedule_risk |
PERT three-point analysis with a Monte Carlo completion distribution and criticality indices |
crash_schedule |
minimum-cost schedule compression to a deadline, solved as a linear program |
earned_value |
the full EVM indicator set with Lipke earned schedule, against a planned-value baseline |
earned_schedule |
the earned schedule for a given earned value |
gantt_chart |
a Gantt chart of the schedule as a PNG image, critical path highlighted |
network_chart |
the activity network with the critical path as a PNG image |
evm_chart |
the earned value S-curve (PV/EV/AC + forecast) as a PNG image |
criticality_chart |
Monte Carlo per-activity criticality bars as a PNG image |
completion_histogram |
Monte Carlo completion-time histogram as a PNG image |
The analysis tools return the library's structured payload: named statistics, a tidy table, structured alerts, and provenance (library version, input hash, timestamp). The chart tools return PNG images the client can display.
Installation
pip install pmcontrols-mcp
Or run it without installing, with uv:
uvx pmcontrols-mcp
Configuration
Add 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.
Example
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": "0.1.0",
"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.
Design
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.
License
MIT. Written and maintained by Atakan Arikan, MSc Student at Tsinghua University and Politecnico di Milano.
Metadata
Release files for pmcontrols-mcp 0.2.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
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Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| pmcontrols_mcp-0.2.1-py3-none-any.whl | Python 3 | none | any | Details for pmcontrols_mcp-0.2.1-py3-none-any.whl |
Total release size: 15.8 kB
Release files / pmcontrols_mcp-0.2.1.tar.gz
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