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
• agentic-sidecar style supervision-readiness and module-surface discovery
• future agenticops-control-tower style operator-facing control-plane access

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.

Connect and use

Start with the user and developer guide for hosted signup, MCP client connection, workflow analysis, evaluation reports, and local chaos/ Sidecar capabilities. Run the Python workflow example to send an exported workflow to the hosted service. The guide documents current integration availability and distinguishes implemented tools from future runtime work.

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
• agentic-sidecar remains the core decision-supervision and governance engine
• agenticops-control-tower remains the future operator-facing control plane
• 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 observability, chaos, sidecar discovery, and later Control Tower-aligned operations surfaces 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
• expose sidecar readiness and scaffold inventory while the upstream runtime is still under construction
• eventually expose Control Tower inventory and operator-facing control surfaces once that sibling package ships them
• 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, agentic-chaos, and agentic-sidecar instead of re-implementing their logic
• Local-first: work well as a stdio MCP server for developer workflows — this matters because chaos.run_experiment executes real code (see SECURITY.md), so this server is meant for trusted, local/stdio use; authenticated HTTP deployments disable code execution
• Dual transport, one tool surface: the optional Streamable HTTP transport (below) reuses stdio tools/resources/prompts, with remote script execution excluded

MCP Surface (current, 0.3.0)

• core.health — rich diagnostics: adapter availability/version, loaded tool/resource/prompt counts, workspace root, recent successful calls
• core.version — server package version
• core.verify — checks agenticlens/agentic-chaos/agentic-sidecar/ai-operations-spec connectivity and reports readiness
• core.session_state — inspect what the active session has accumulated
• lens.analyze_workflow — run AgenticLens recommendations against a workflow artifact
• lens.report_summary — render a Markdown workflow report
• lens.compare_runs — compare baseline/candidate trace runs for regressions
• lens.slo_summary — apply release-gate style SLO thresholds to an evaluation report
• lens.audit_report — case-by-case evaluation detail, optionally with HTML
• chaos.list_faults — list the supported fault types
• chaos.run_experiment — run a workspace-sandboxed target script under selected faults (executes real code — see SECURITY.md)
• sidecar.status — report whether agentic-sidecar is connected and whether its runtime is implemented yet (added in 0.3.0)
• sidecar.module_inventory — inspect the current scaffolded sidecar modules, framework adapters, and integration placeholders (added in 0.3.0)
• spec.validate_artifact — validate a workflow/run artifact against the AI Operations v0.4 draft

Sequential tool calls can share context via an optional session_id argument, backed by an in-memory session store — see ROADMAP.md Phase 2.

See ROADMAP.md for what's shipped per phase and what's still open, and docs/tools.md for full input schemas and per-tool metadata (generated from tools/registry.py, run make docs to refresh it after changing that file).

Local vs. remote install modes

Container definitions are grouped under deploy/docker.

• Local: pip install deep-agentic-core-mcp, then run deep-agentic-core-mcp over stdio. Local session behavior is unchanged.
• Self-hosted multi-user HTTP (0.3.0+): install with pip install 'deep-agentic-core-mcp[http]', then run deep-agentic-core-mcp-http. Provision a unique bearer key per user and a Redis URL before startup. Missing configuration prevents startup.
• Hosted AWS signup: open mcp.deepagentlabs.io to generate a user identity and MCP key; connect your MCP client to https://mcp.deepagentlabs.io/mcp with its bearer key. DynamoDB stores user records and key hashes; keys work immediately and can be replaced or revoked through the page. No password or email verification is used. Save the key: it cannot be recovered. See the AWS guide.

The HTTP service uses stateless MCP transport at /mcp and Redis-backed workflow sessions scoped to the authenticated user. Users can reuse the same session_id without accessing each other's artifacts. HTTP calls cannot run chaos.run_experiment, even if the local remote-chaos override is set.

See the remote hosting guide and render.yaml for Render deployment, configuration, container builds, AWS deployment requirements, client headers, limits, and credential rotation. create_app() also accepts explicit credentials, a session backend, and allowed hosts/origins for integrations and tests. MemorySessionStore is an explicit development option; the CLI requires Redis.

Repository Layout

mcp-server/
├── README.md
├── ROADMAP.md
├── pyproject.toml
├── server.json
├── .gitignore
├── docs/
│   ├── architecture.md
│   └── tools.md          # generated - see scripts/generate_tools_doc.py
├── examples/
│   ├── sample_workflow.json
│   └── chaos_target.py
├── scripts/
│   └── generate_tools_doc.py
├── src/
│   └── deep_agentic_core_mcp/
│       ├── __init__.py
│       ├── server.py
│       ├── transport_http.py
│       ├── config.py
│       ├── prompts/
│       │   ├── __init__.py
│       │   └── registry.py
│       ├── resources/
│       │   ├── __init__.py
│       │   └── catalog.py
│       ├── schemas/
│       │   ├── __init__.py
│       │   └── tooling.py
│       ├── services/
│       │   ├── __init__.py
│       │   ├── registry.py
│       │   └── session.py
│       ├── adapters/
│       │   ├── __init__.py
│       │   ├── agentic_chaos.py
│       │   ├── agenticlens.py
│       │   ├── agentic_sidecar.py
│       │   └── ai_operations_spec.py
│       └── tools/
│           ├── __init__.py
│           ├── registry.py
│           ├── chaos.py
│           ├── core.py
│           ├── lens.py
│           ├── sidecar.py
│           └── spec.py
└── tests/
    ├── test_degraded_boot.py
    ├── test_imports.py
    ├── test_registry.py
    ├── test_server.py
    └── test_session.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, including the in-memory session store (services/session.py)
• adapters/ for integration boundaries to agenticlens, agentic-chaos, agentic-sidecar, and ai-operations-spec — each degrades to "available": false rather than crashing server boot if its sibling repo is missing

Packaging and Publishing Model

One repository supports both the local Python package and the hosted HTTP service. The stdio entry point remains available without the optional HTTP dependencies. HTTP transport and signup support are included from 0.3.0; the earlier PyPI 0.2.0 release provides local stdio.

Workflow	Trigger	Result
.github/workflows/ci.yml	Pull request	Tests Python 3.10–3.13 and builds distributions; no AWS deployment
.github/workflows/ci.yml	Push/merge to main, or manual run	Runs tests and package checks; no AWS deployment
.github/workflows/release-pypi.yml	Push a v* version tag	Runs checks, publishes to PyPI, creates the GitHub Release, publishes MCP Registry metadata, and deploys AWS after PyPI succeeds

A push to main does not publish to PyPI or deploy AWS. A version-tag release deploys the same tagged source to AWS only after PyPI publication succeeds. Package releases use PyPI Trusted Publishing; AWS deployment uses the dedicated IAM user's GitHub secrets. See CONTRIBUTING.md for releases and the AWS CI guide for deployment setup and limits.

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

What's Next

Phase 2 (session management, rich diagnostics, tool annotations, prompt registry, core.verify) and Phase 3b (Agentic Chaos) are complete as of 0.2.0. What's still open (see ROADMAP.md for full detail):

• Phase 3a (AgenticLens) — provenance verification on lens.analyze_workflow's response shape
• Phase 3d (Agentic Sidecar Discovery) — now implemented in the current development line; richer sidecar control surfaces still depend on upstream runtime milestones landing first
• Phase 3c (AI Operations Specification) — multi-version schema support and conformance-style reporting, both blocked on upstream ai-operations-spec work landing first
• Phase 4 (Unified Workflows) — joined observability + chaos workflows, incident/readiness reporting, a higher-level control surface
• Future Control Tower coordination — once agenticops-control-tower ships real control-plane APIs, MCP should expose those operator-facing surfaces without reimplementing them here
• Phase 5/6 — PyPI + MCP Registry publishing, operational intelligence features

Development

A Makefile provides shorthand for common tasks:

make install     # install dev dependencies
make check       # run all quality gates (lint + format + typecheck + test)
make test-cov    # tests with coverage report
make docs        # regenerate docs/tools.md from tools/registry.py
make docs-check  # fail if docs/tools.md is out of date
make help        # list all available targets

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