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Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
<!-- mcp-name: io.github.arikanatakan/pmcontrols-mcp -->
pmcontrols-mcp
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.
pmcontrols-mcp architecture: an AI agent calls the server's analysis and chart tools, which route to the validated pmcontrols core and return structured JSON or PNG images
Tools
Analysis tools return the library's structured payload: named statistics, a tidy table, structured alerts, and provenance (library version, input hash, timestamp).
| 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 |
Chart tools return a PNG image the client can display.
| Tool | Purpose |
|---|---|
gantt_chart | a Gantt chart of the schedule, critical path highlighted |
network_chart | the activity network with the critical path |
evm_chart | the earned value S-curve (PV/EV/AC + forecast) |
criticality_chart | Monte Carlo per-activity criticality bars |
completion_histogram | Monte Carlo completion-time histogram |
Installation
pip install pmcontrols-mcpOr run it without installing, with uv:
uvx pmcontrols-mcpConfiguration
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": "0.2.1",
"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.
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.
Related
pmcontrols is the underlying library this server wraps.
License
MIT. Written and maintained by Atakan Arikan, MSc Student at Tsinghua University and Politecnico di Milano.