PM Governance MCP Server
Enables AI-assisted project management governance across 8 domains including projects, RAID, scope, people, time, cost, meetings, and administration through 51 MCP tools.
README
PM Governance MCP Server v3
A complete Model Context Protocol (MCP) server implementation for AI-assisted project management governance across 8 domains: Projects, Governance (RAID), Scope (Goals→KPIs), People (Teams & Availability), Time (Milestones & Demand Planning), Cost (Procurement & Contracts), Meetings, and Administration.
Architecture
MCP Layer (51 tools)
↓
Services Layer (10 services + dashboard)
↓
Repository Layer (8 typed protocols + base class)
↓
Cache Layer (memory, Redis, tiered)
↓
HTTP Client Layer (httpx with retries, auth, error mapping)
↓
REST Backend (JSON Server-compatible API)
Quick Start
-
Install dependencies:
pip install -e . -
Configure environment:
cp .env.example .env # Edit .env with your API credentials -
Run the server:
# Stdio transport (default) python main.py # Or SSE transport (for web clients) python main.py --transport sse --port 8080
Key Features
- 51 MCP Tools across 8 domains (project, governance, scope, people, time, cost, meeting, admin, dashboard)
- Typed Repository Protocols — inject test mocks without subclassing
- Multi-tier Caching — memory L1 + Redis L2 with TTL per data type
- Input Guardrails — prompt injection detection, validation bounds
- Structured Logging — JSON output via structlog + stdlib
- Telemetry Integration — W&B Weave + OpenTelemetry (optional, no-op if disabled)
- Full DI Container — Settings → ApiClient → Repos → Services → Tools
- MCP Resources — 4 markdown guides embedded in context (API overview, portfolio, governance, scope)
- MCP Prompts — 4 reusable prompt templates (project report, executive briefing, RAID escalation, scope progress)
Domain Model
Projects
- RAG status (overall, schedule, budget, risk)
- Budget baseline / actual / forecast
- Ownership and methodology
Governance (RAID + Change)
- Risks — probability, impact, mitigation, escalation
- Issues — severity, resolution, escalation
- Dependencies — incoming/outgoing/external
- Decisions — meeting log, impact
- Actions — score-based (probability × impact), category, approval gates
- Change Requests — workflow (Submitted → Approved → Implemented)
Scope (Goal Hierarchy)
- Goals — strategic, with status
- Objectives — measurable outcomes per goal
- Benefits — business value per objective
- KPIs — baseline/target/current per benefit
- Stories — Functional or Technical, linked to objectives
- Tasks — estimated hours per story/scope item
- Scope Items — deliverables with status and priority
People (Teams & Availability)
- Teams — named groups with metadata
- Members — hourly rate, weekly availability, role
- Roles — catalogue entries (PM, BA, Developer, etc.)
- Absences — vacation, sick, training, personal with approval status
Time (Schedule & Effort)
- Milestones — with status (On Track, At Risk, Delayed, Completed)
- Demand Planning — hours grid (taskId → {memberKey: hours})
- Timesheets — actual hours + ETC grid
Cost
- Procurement — invoices, vendor spend, cost categories
- Contracts — vendor agreements, lifecycle (Draft → Active → Expired)
Meetings
- Meetings — steering committee, weekly status, stakeholder review
- Reports — published governance documents
- Lessons Learned — approved post-project retrospectives
Admin
- Roles — platform and project roles
- Privileges — granular permission catalogue
- Audit Logs — immutable operation history
Testing
Run the test suite:
pytest tests/ -v
Key fixtures:
test_settings— test-mode configuration (null cache, fake backend)mock_api_client— httpx client for testingnull_cache— deterministic NullCacheBackend
Environment Variables
See .env.example for all options. Key variables:
| Variable | Default | Purpose |
|---|---|---|
API_BASE_URL |
http://localhost:4000 |
PM Governance REST backend |
API_KEY |
(empty) | Bearer token + X-Api-Key header |
CACHE_BACKEND |
memory |
Cache strategy (memory / redis / tiered) |
LOG_LEVEL |
INFO |
Logging verbosity |
LOG_DEV_MODE |
false |
Console renderer (true) vs JSON (false) |
WANDB_API_KEY |
(empty) | W&B Weave tracing (optional) |
OTEL_ENABLED |
false |
OpenTelemetry tracing (optional) |
File Structure
pm-governance-mcp/
├── main.py # Entry point
├── pyproject.toml # Package metadata
├── .env.example # Environment template
├── README.md # This file
├── src/pm_mcp/
│ ├── __init__.py
│ ├── container.py # DI container
│ ├── config/
│ │ └── settings.py # Pydantic-settings
│ ├── client/
│ │ └── api_client.py # httpx with retry
│ ├── cache/
│ │ └── ttl_cache.py # Memory / Redis / Tiered
│ ├── exceptions/
│ │ ├── base.py
│ │ ├── infrastructure.py
│ │ ├── domain.py
│ │ ├── validation.py
│ │ └── mcp.py
│ ├── guardrails/
│ │ └── input_guard.py # Injection detection
│ ├── models/
│ │ ├── common.py
│ │ ├── projects.py
│ │ ├── governance.py
│ │ ├── scope.py
│ │ ├── people.py
│ │ ├── time_.py
│ │ ├── cost.py
│ │ ├── meetings.py
│ │ └── admin.py
│ ├── repositories/
│ │ ├── protocols.py # 8 typed Protocols
│ │ ├── base.py
│ │ ├── project_repo.py
│ │ ├── governance_repo.py
│ │ ├── scope_repo.py
│ │ ├── people_repo.py
│ │ ├── time_repo.py
│ │ ├── cost_repo.py
│ │ ├── meeting_repo.py
│ │ └── admin_repo.py
│ ├── services/
│ │ ├── project_service.py
│ │ ├── governance_service.py
│ │ ├── scope_service.py
│ │ ├── people_service.py
│ │ ├── time_service.py
│ │ ├── cost_service.py
│ │ ├── meeting_service.py
│ │ ├── admin_service.py
│ │ └── dashboard_service.py
│ ├── tools/
│ │ ├── base_tool.py # BaseTool ABC
│ │ ├── project_tool.py
│ │ ├── governance_tool.py
│ │ ├── scope_tool.py
│ │ ├── people_tool.py
│ │ ├── time_tool.py
│ │ ├── cost_tool.py
│ │ ├── meeting_tool.py
│ │ ├── admin_tool.py
│ │ └── dashboard_tool.py
│ ├── registry/
│ │ ├── tool_registry.py
│ │ ├── resource_registry.py
│ │ └── prompt_registry.py
│ ├── server/
│ │ ├── app.py # FastMCP app + run()
│ │ ├── lifespan.py # Startup/shutdown
│ │ ├── resources.py # MCP resources
│ │ ├── prompts.py # MCP prompts
│ │ └── content/
│ │ ├── api_overview.md
│ │ ├── portfolio_guide.md
│ │ ├── governance_guide.md
│ │ └── scope_guide.md
│ └── telemetry/
│ ├── logging_.py
│ ├── weave_.py
│ └── otel.py
└── tests/
├── conftest.py
├── unit/
│ ├── tools/
│ ├── services/
│ ├── repositories/
│ ├── client/
│ └── guardrails/
├── integration/
└── contract/
Deployment
Docker
FROM python:3.12-slim
WORKDIR /app
COPY . .
RUN pip install -e .
CMD ["python", "main.py"]
As MCP Server in Claude Desktop
Add to claude_desktop_config.json:
{
"mcpServers": {
"pm-governance": {
"command": "python",
"args": ["/path/to/pm-governance-mcp/main.py"],
"env": {
"API_BASE_URL": "https://pm-api.example.com",
"API_KEY": "sk-xxxx"
}
}
}
}
License
MIT
Support
For issues or questions, refer to ARCHITECTURE.md and openapi.yaml in the repository root.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.