Metadata-Version: 2.5
Name: deep-agentic-core-mcp
Version: 0.3.7
Summary: Unified MCP server for AgenticLens and Agentic Chaos workflows.
Project-URL: Homepage, https://github.com/DeepAgentLabs/mcp-server
Project-URL: Repository, https://github.com/DeepAgentLabs/mcp-server
Project-URL: Issues, https://github.com/DeepAgentLabs/mcp-server/issues
Author: DeepAgentLabs
License: MIT
Keywords: agentic-ai,chaos-engineering,llm,mcp,observability
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.10
Requires-Dist: jsonschema<5,>=4.23
Requires-Dist: mcp<3,>=2.0.0
Requires-Dist: pydantic<3,>=2.0
Requires-Dist: referencing<1,>=0.35
Provides-Extra: dev
Requires-Dist: build>=1.2; extra == 'dev'
Requires-Dist: fakeredis[lua]<3,>=2.23; extra == 'dev'
Requires-Dist: httpx<1,>=0.27; extra == 'dev'
Requires-Dist: moto[dynamodb]<6,>=5; extra == 'dev'
Requires-Dist: mypy>=1.10; extra == 'dev'
Requires-Dist: pytest-asyncio>=0.23; extra == 'dev'
Requires-Dist: pytest-cov>=5.0; extra == 'dev'
Requires-Dist: pytest>=8.0; extra == 'dev'
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Provides-Extra: http
Requires-Dist: boto3<2,>=1.34; extra == 'http'
Requires-Dist: redis<7,>=5; extra == 'http'
Requires-Dist: starlette>=0.37; extra == 'http'
Requires-Dist: uvicorn>=0.30; extra == 'http'
Description-Content-Type: text/markdown

# 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
- authenticated local Control Tower 0.6.0 investigation and approved recovery 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.

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

## Connect and use

Start with the [user and developer guide](docs/user-guide.md) for hosted signup,
MCP client connection, workflow analysis, evaluation reports, and local chaos/
Sidecar capabilities. Run [the Python workflow example](examples/hosted_workflow.py)
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` 0.6.0 provides the 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 installed Sidecar decision/runtime capabilities and module inventory;
  package readiness is distinct from a live task connection
- 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](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

## Agentic Chaos v0.5 compatibility (MCP 0.3.2)

Install `agentic-chaos>=0.5.0` alongside the server to discover streaming,
authentication, and context-length faults through `chaos.list_faults`.
The existing local stdio `chaos.run_experiment` tool runs
[`examples/chaos_stream_target.py`](examples/chaos_stream_target.py) with
`faults=["stream_cut"]` and returns the native degraded event. Real sibling
integration tests cover the catalog and experiment path. Chaos remains optional;
no hard sibling dependency is introduced. Hosted HTTP rejects chaos execution.

The example is a repository file, not a wheel-installed asset. Clone the
`v0.3.2` source into a `mcp-server` directory and set
`DEEP_AGENTIC_CORE_MCP_WORKSPACE_ROOT` to its parent directory before starting
the server. The [user guide](docs/user-guide.md#chaos-v05-streaming-with-mcp-032)
provides exact setup commands. CI checks both the pinned Chaos `v0.5.0` tag
and the latest sibling checkout.

See [the user guide](docs/user-guide.md#chaos-v05-streaming-with-mcp-032) and
[the Chaos v0.5 API guide](https://github.com/DeepAgentLabs/agentic-chaos/blob/main/docs/v0.5.md).

## Phase 3 completion (MCP 0.3.3)

- **Finding provenance (3a):** every `lens.analyze_workflow` recommendation has
  a `provenance` block. It names the artifact (`workflow_id`, canonical
  `sha256` digest), the workflow step (`id`, `name`, `type`, `index`), the
  evidence span IDs and the producer. `resolved` is true only when all of these
  exist in the artifact you sent, and `provenance_summary` counts how many do.
  Needs `agenticlens>=0.6.1` for model-swap and chaos-impact findings.
- **Chaos results as AIOS runs (3b):** `chaos.run_experiment` attaches
  `aios_run`, an AI Operations v0.4-draft Run built by `agentic-chaos>=0.5.1`'s
  own exporter and checked by the spec adapter (`aios_export.aligned`).
  `chaos.export_aios_run` converts a saved or session report without running
  any code.
- **Script policy (3b):** `DEEP_AGENTIC_CORE_MCP_ALLOWED_SCRIPT_GLOBS` and
  `DEEP_AGENTIC_CORE_MCP_DENIED_SCRIPT_GLOBS` narrow which workspace scripts
  `chaos.run_experiment` may run. Deny wins, and `.venv`, `site-packages` and
  `.git` are always denied.
- **Version routing and conformance (3c):** `spec.validate_artifact`
  validates against the schema set the artifact's `spec_version` names and
  rejects markers it has no schema for, citing the applicable v0.5 rule
  (`AIOS-0.5-V01`…`V06`). Its `conformance` section lists every
  `AIOS-0.4-*` rule with a status, separate from server presentation. Needs
  `ai-operations-spec>=0.5.1` for the rule and compatibility catalogs.

Run [`examples/phase3_walkthrough.py`](examples/phase3_walkthrough.py) from a
workspace checkout to see all four end to end.

Since `0.3.4` the hosted service at `https://mcp.deepagentlabs.io/mcp` ships
the same sibling versions and spec catalogs, so provenance, conformance reports
and `chaos.export_aios_run` behave the same hosted as locally. Only
`chaos.run_experiment` stays local-only.

## AgenticLens 0.7.0 diagnosis tools (MCP 0.3.5)

- **`lens.diagnose_run`** — send a trace `Run` (and optional `baseline` runs)
  and get AgenticLens's diagnosis report: failure-taxonomy diagnoses with
  confidence and supporting spans, the faulty step and agent with ranked
  candidates, the incident timeline, robust anomalies against the baseline,
  guardrail status (`budget` caps spans, depth, and time), and an
  investigation narrative in Markdown (`include_narrative`, default true).
- **`lens.score_analysis`** — send an evaluation report and get deterministic
  and model-based scores reported separately, judge cost kept apart from agent
  cost, and the cases where evaluators disagree.

Both need `agenticlens>=0.7.0`; with an older install they return a clear
"unavailable" error and every other Lens tool keeps working. The hosted images
pin `agenticlens==0.7.0` and `agentic-chaos==0.6.0` (since MCP 0.3.7).

## MCP Surface (current, `0.3.7`)

- `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
- `lens.diagnose_run` — diagnose a trace run: faulty step and agent, timeline,
  anomalies, investigation narrative (`agenticlens>=0.7.0`)
- `lens.score_analysis` — deterministic vs model-based scores, judge cost, and
  evaluator disagreement for an evaluation report (`agenticlens>=0.7.0`)
- `chaos.list_faults` — list the supported fault types
- `chaos.run_experiment` — run a workspace-sandboxed, policy-checked target
  script under selected faults and attach an AIOS run export
  ([executes real code — see `SECURITY.md`](SECURITY.md))
- `chaos.export_aios_run` — convert a chaos report into a validated AI
  Operations v0.4-draft run artifact (added in `0.3.3`; no code execution)
- `sidecar.status` — report installed Sidecar runtime APIs separately from
  live task connectivity (added in `0.3.0`)
- `sidecar.module_inventory` — inspect the installed sidecar
  modules, framework adapters, and integration placeholders (added in `0.3.0`)
- `spec.validate_artifact` — validate a workflow/run artifact against the
  schema set its `spec_version` names, with a rule-cited conformance report

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](ROADMAP.md) for what's shipped per phase and what's still
open, and [docs/tools.md](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](deploy/docker/README.md).

- **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](https://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](deploy/aws/README.md).

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](docs/remote-hosting.md) 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

```text
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
│   ├── provenance_workflow.json
│   ├── phase3_walkthrough.py
│   └── 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_chaos_aios_export.py
    ├── test_chaos_script_policy.py
    ├── test_degraded_boot.py
    ├── test_imports.py
    ├── test_lens_provenance.py
    ├── test_spec_conformance.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](CONTRIBUTING.md) for releases and
[the AWS CI guide](deploy/aws/CI-CD.md) 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`) is complete as of `0.2.0`, and Phase 3 (3a AgenticLens,
3b Agentic Chaos, 3c AI Operations Specification, 3d Sidecar discovery) is
complete as of `0.3.3`. What's still open (see [ROADMAP.md](ROADMAP.md) for
full detail):

- **Sidecar runtime controls**: richer control surfaces require an
  authenticated, task-scoped runtime bridge
- **Phase 4 (Unified Workflows)** — joined observability + chaos workflows,
  incident/readiness reporting, a higher-level control surface
- **Control Tower coordination** — local connector tools ship in 0.3.6 for
  Tower 0.6.0; hosted per-user credential binding remains planned
- **Phase 5/6** — PyPI + MCP Registry publishing, operational intelligence
  features

## Development

A `Makefile` provides shorthand for common tasks:

```bash
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`

## Operational safety roadmap

See [planned safety acceptance criteria](ROADMAP.md#access-containment-and-evidence-planned-extensions) for responsibilities and
dependencies. These additions do not claim implemented end-to-end containment
or integrity protection.

### Sidecar v0.7 compatibility

With Sidecar installed, `sidecar.status` detects its public decision, plan,
runtime-event, audit and local operator APIs. `runtime_ready` means the decision
API is installed; `runtime_connected=false` and `mcp_runtime_controls=false`
make clear that these MCP discovery tools do not attach to an active task.
`sidecar.module_inventory` lists adapter files, including placeholders; their
presence does not certify framework support. No Sidecar dependency is required
for MCP startup. An unavailable package produces an unavailable response.

For live task controls, use Sidecar's authenticated local OperatorServer/CLI
interface described in its
[v0.7.0 live operations guide](https://github.com/DeepAgentLabs/agentic-sidecar/blob/v0.7.0/docs/live-operations.md).
Sidecar v0.7.0 is published separately. This patch improves discovery without
enabling remote stop/revoke through MCP.

## Sidecar 0.8 interoperability

MCP server 0.3.5 retains connectivity, discovery and transport ownership.
Sidecar 0.8 adds a client adapter and a separate-process MCP 2.3.0 gateway example
with server-side enforcement. This does not enable remote operator bridging
in this server or expose shared/exclusive configuration through public tools.

Sidecar 0.8 provides Shared/Exclusive context-only, guardrails-only or combined
configuration, explicit host context delivery and mandatory guardrail composition.
Exclusive rules cannot weaken shared restrictions. Cross-framework privacy,
revocation and delegated authority remain v0.9 validation; stable schemas remain
v1.0. Native correlated Lens readback requires Lens 0.7.1. All sibling packages
stay optional; no dependency floor is increased without a runtime requirement.
See the [Sidecar integration contract](https://github.com/DeepAgentLabs/agentic-sidecar/blob/main/docs/integrations.md).

## Control Tower 0.6.0 connector (MCP 0.3.6)

Local stdio exposes twelve `tower.*` tools over the actual authenticated Tower
HTTP API: inventory/status/versions/posture/evidence, incidents, detection,
investigation, preview, execute and verify. Configure `AGENTICOPS_TOWER_URL`
(a trusted HTTP(S) origin), `AGENTICOPS_TOWER_READ_TOKEN`, and optionally the
separate `AGENTICOPS_TOWER_WRITE_TOKEN`. No Tower package import is required.
Without Tower, all other tools work; Tower tools return structured unavailable.

The `tower.investigation` prompt directs the host model to cite record IDs and
artifact references, preserve uncertainty and missing evidence, recommend only
the supported staging runbook, and leave approval to a human through Tower's
HTTP/CLI. Approval credentials are never accepted by tool arguments or exposed
through an approval tool. Execution is subject to Tower's exact-preview approval.
The model provider remains the client's choice; none is bundled.

Hosted/remote MCP rejects Tower tools until per-user Tower credential binding
exists. Local tokens cannot be borrowed by hosted tenants. Redirects are refused,
responses are limited to 1 MiB and an isolated HTTP worker enforces a ten-second
total deadline. A timeout may leave an action result unknown; inspect the Tower
incident before retrying. URLs and tokens
come only from trusted server environment. The Tower optional connector extra
must use MCP >=0.3.6; other producer floors remain unchanged.

For setup, request JSON and both resolved/escalated native Lens/Evals walkthroughs,
see [Tower's connector guide](https://github.com/DeepAgentLabs/agenticops-control-tower/blob/main/docs/mcp-connector.md)
and [incident guide](https://github.com/DeepAgentLabs/agenticops-control-tower/blob/main/docs/incident-recovery.md).
Use `tower.agents` with `status=unhealthy`, `tower.versions` with an explicit
minimum, or `tower.evidence` with `source=agentic-sidecar` to inspect high-risk
posture without replacing native producer decisions.
