<!-- mcp-name: io.github.arikanatakan/pmcontrols-mcp -->

# pmcontrols-mcp

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An MCP server that exposes [pmcontrols](https://github.com/arikanatakan/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](assets/architecture.png)

## 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-mcp
```

Or run it without installing, with [uv](https://docs.astral.sh/uv/):

```
uvx pmcontrols-mcp
```

## Configuration

Add the server to your MCP client's configuration:

```json
{
  "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:

```json
{
  "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](https://arikanatakan.github.io/pmcontrols/agents/).

## Related

[pmcontrols](https://github.com/arikanatakan/pmcontrols) is the underlying
library this server wraps.

## License

MIT. Written and maintained by [Atakan Arikan](https://github.com/arikanatakan),
MSc Student at Tsinghua University and Politecnico di Milano.