deployment-intelligence-mcp

Turn DevOps configuration into Deployment Knowledge.

Why does this exist?

CodeGraph answers:

Who calls CreateAccountUseCase()?

Deployment Intelligence MCP answers:

How does CreateAccountUseCase reach Production?

This project exists to connect code and deployment artifacts across the delivery chain. It is about the knowledge between code, build, workflow, deployment, service, and ingress. YAML is an implementation detail; the product is Deployment Knowledge.

Related MCP server: NEAT MCP Server

What it does today

This repository extracts deployment knowledge from a local repository and caches it in SQLite. The current implementation supports:

• GitHub Actions workflows under .github/workflows/

• Kubernetes manifests under k8s/

• a root Dockerfile

It exposes a query layer for MCP agents to inspect the knowledge cache, not to directly search raw YAML.

High-level flow

Repository
     │
     ▼
Indexer
     │
     ▼
Knowledge DB
     │
     ▼
Query Engine
     │
     ▼
MCP

Primary abstractions

• Workflow

• Job

• Step

• Image

• Deployment

• Service

Current CLI commands

Install the project:

python -m pip install -e .[dev]

Show CLI help:

dimcp --help

Index the current repository:

dimcp index .

Run the MCP server:

dimcp serve

Current MCP tools

The server exposes the following tools:

• list_workflows

• get_workflow

• list_services

• list_deployments

• list_images

What’s next

Planned v0.1 improvements:

• dimcp inspect for quick knowledge summaries

• dimcp explain <target> for deployment explanations

• trace-first queries like trace_service() and trace_image()

• a .dimcp/ cache location with graph.db and metadata

• a stronger bridge from methods to services and back

Testing

Run the test suite:

pytest -q