deep-agentic-core-mcp

deep-agentic-core-mcp is the shared MCP server layer for the DeepAgentLabs ecosystem. It is designed to expose a single MCP interface that combines:

• agenticlens style workflow inspection, profiling, and analysis
• agentic-chaos style resilience testing and fault-injection workflows

It sits above the AI Operations Workflow Specification, exposing a unified MCP-native control surface over the shared operational model used by the reference implementations.

The goal is one MCP server, one package, and one registry identity rather than separate MCP servers for each product surface.

<!-- mcp-name: io.github.DeepAgentLabs/deep-agentic-core-mcp -->

Idea

This project is the control plane between LLM hosts and the existing Python libraries:

• agenticlens remains the core profiling and analysis engine
• agentic-chaos remains the core chaos and resilience engine
• the AI Operations Workflow Specification remains the shared data contract
• deep-agentic-core-mcp becomes the MCP-native interface that hosts can call

That means MCP clients can connect once and access both observability and chaos testing capabilities through one server.

What This Server Should Eventually Do

Planned capability areas:

• profile an agentic workflow and return structured telemetry summaries
• analyze workflow artifacts and surface optimization recommendations
• run controlled chaos experiments against target workflows
• compare normal versus chaos runs
• expose shared resources such as workflow schemas, run metadata, and saved reports

Design Principles

• One MCP identity: publish a single server to the MCP Registry
• Python-first: package and publish through PyPI
• Thin orchestration layer: reuse agenticlens and agentic-chaos instead of re-implementing their logic
• Local-first: work well as a stdio MCP server for developer workflows
• Expandable: leave room for a later remote deployment mode if needed

Initial Scope

The first milestone is foundation only:

• repository structure
• packaging metadata
• MCP registry metadata
• roadmap and product framing
• minimal server entrypoint and tool layout

The first working implementation can stay intentionally small while the shape of the tool surface stabilizes.

Proposed MCP Surface

Possible first tool groups:

• lens.profile_workflow
• lens.analyze_workflow
• chaos.run_experiment
• chaos.list_faults
• core.health
• core.version

These names are placeholders, but the structure matters: one server can expose multiple tools without needing multiple MCP packages or registry entries.

Repository Layout

mcp-server/
├── README.md
├── ROADMAP.md
├── pyproject.toml
├── server.json
├── .gitignore
├── docs/
│   └── architecture.md
├── examples/
│   └── sample_workflow.json
├── src/
│   └── deep_agentic_core_mcp/
│       ├── __init__.py
│       ├── server.py
│       ├── config.py
│       ├── prompts/
│       │   ├── __init__.py
│       │   └── registry.py
│       ├── resources/
│       │   ├── __init__.py
│       │   └── catalog.py
│       ├── schemas/
│       │   ├── __init__.py
│       │   └── tooling.py
│       ├── services/
│       │   ├── __init__.py
│       │   └── registry.py
│       ├── adapters/
│       │   ├── __init__.py
│       │   ├── agentic_chaos.py
│       │   └── agenticlens.py
│       └── tools/
│           ├── __init__.py
│           ├── registry.py
│           ├── chaos.py
│           ├── core.py
│           └── lens.py
└── tests/
    ├── test_imports.py
    └── test_registry.py

MCP-Oriented Structure

This repository should have all of the standard layers we expect for a useful MCP server:

• tools/ for callable MCP tools and their registration metadata
• resources/ for readable assets such as fault catalogs, templates, and workflow examples
• prompts/ for reusable prompt templates exposed through the server
• schemas/ for typed request and response contracts
• services/ for shared orchestration logic that keeps tool modules thin
• adapters/ for integration boundaries to agenticlens and agentic-chaos

The implementation is still early, but the file structure now reflects that shape so we can add functionality without reshuffling the repo later.

Packaging and Publishing Model

deep-agentic-core-mcp should publish in two layers:

1. Publish the Python package to PyPI.
2. Publish the MCP metadata in server.json to the official MCP Registry.

For PyPI-based verification, the mcp-name marker above must match the name field in server.json.

Near-Term Build Order

1. Lock the canonical namespace and package metadata.
2. Implement the stdio MCP server entrypoint.
3. Add a minimal core.health tool.
4. Add the first agenticlens and agentic-chaos adapter-backed tools.
5. Add examples and publishable packaging checks.

Notes

This scaffold assumes the intended GitHub namespace is io.github.deepagentlabs/deep-agentic-core-mcp. If the final publishing account or org changes, update:

• the mcp-name marker in this README
• server.json
• any repository URLs in pyproject.toml