Skip to main content

pmcontrols-mcp

CI PyPI License: MIT

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)

Source distribution for pmcontrols-mcp 0.2.1
File Size Uploaded Provenance Actions
pmcontrols_mcp-0.2.1.tar.gz 7.7 kB With provenance Details for pmcontrols_mcp-0.2.1.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for pmcontrols-mcp 0.2.1
File Interpreter ABI Platform Actions
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

Download URL pmcontrols_mcp-0.2.1.tar.gz
Size 7.7 kB
Tags Source
SHA-256 checksum
How to use checksums
9604240a75c7b09f60494521c62ad56c06a4deac60413118343f711ed61bf07e
BLAKE2b-256 checksum
How to use checksums
105cd39d10cecfe0d1351994aee28ec4a5e68847787b0445e04df76d01a17c52
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.12

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jun 15, 2026.

Transparency log

Release files / pmcontrols_mcp-0.2.1-py3-none-any.whl

Download URL pmcontrols_mcp-0.2.1-py3-none-any.whl
Size 8.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
baa6c5903dbeddbcbdc2094f63ccdb3749c5e82b956aa09ea033136537d4be1f
BLAKE2b-256 checksum
How to use checksums
e62c990e29df3c750a06d4f40d6695dafa384f4d119676402678ca54ff096f01
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.12

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jun 15, 2026.

Transparency log

Security for pmcontrols-mcp 0.2.1

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Full provenance

All 2 files in this release have been uploaded with provenance metadata.

Select from 2 files

Attestation for pmcontrols_mcp-0.2.1.tar.gz

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Jun 15, 2026.

Transparency log

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page