Extracts deployment knowledge from GitHub Actions workflows, including jobs, steps, and images to trace how code reaches production.
Parses Kubernetes manifests to model deployments, services, and ingresses for deployment knowledge.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@deployment-intelligence-mcp How does the authentication service reach production?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Turn DevOps configuration into Deployment Knowledge.
CodeGraph answers:
Who calls
CreateAccountUseCase()?
Deployment Intelligence MCP answers:
How does
CreateAccountUseCasereach 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: prayog-repo-fleet
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.
Repository
│
▼
Indexer
│
▼
Knowledge DB
│
▼
Query Engine
│
▼
MCPWorkflow
Job
Step
Image
Deployment
Service
Install the project:
python -m pip install -e .[dev]Show CLI help:
dimcp --helpIndex the current repository:
dimcp index .Run the MCP server:
dimcp serveThe server exposes the following tools:
list_workflows
get_workflow
list_services
list_deployments
list_images
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
Run the test suite:
pytest -qThis server cannot be deployed
Repository knowledge graph MCP server for codebase understanding and debugging.
Repository evidence for agents before they adopt dependencies, enter codebases, compare, or merge.
The Cortex MCP server provides read-only access to real-time engineering context from the Cortex developer portal, allowing AI coding assistants to answer natural language questions about your organization's catalog (microservices, libraries, domains, teams, infrastructure), scorecards (engineering standards and best practices), initiatives (goals and deadlines), and Engineering Intelligence metrics. It includes tools for querying documentation, tracking personal entities, and accessing AI-assisted insights across the entire Cortex ecosystem.
Discover Agents and MCP capabilities with versions, permissions, and real-work trust context.
