# Deep Agentic Core MCP

One MCP server for agentic workflow profiling, fault-injection experiments and AI Operations spec checks, so your client connects once.

- MCP server · `type=mcp` · Claude Code, Codex, Cursor, GitHub Copilot, Windsurf (Devin Desktop), Cline, Zed · ★ 66
- Author: DeepAgentLabs
- License: not stated · policy=link-only · description only; the file stays with the author
- Source: https://github.com/DeepAgentLabs/mcp-server
- Updated: 2026-10-21
- HTML: https://affpapa.org/en/nevibecode/mcp/deep-agentic-core-mcp

## What it does

The server folds several product surfaces into a single MCP identity: workflow inspection and profiling in the `agenticlens` style, resilience testing with fault injection in the `agentic-chaos` style, module-surface and supervision-readiness discovery in the `agentic-sidecar` style, plus authenticated read-only access to Control Tower 0.7.0. It does not reimplement any of that logic. This is a thin orchestration layer over the existing Python libraries, packaged through PyPI and meant to run as a stdio server.

Tools are grouped by prefix. `lens.analyze_workflow` returns recommendations with a `provenance` block naming the artifact, the workflow step, the evidence span IDs and the producer, while `provenance_summary` counts how many of those were actually found in the artifact you sent. `lens.diagnose_run` takes a trace `Run` plus optional baseline runs and returns failure-taxonomy diagnoses with confidence, the faulty step and agent, an incident timeline and a Markdown narrative. `spec.validate_artifact` validates against the schema set named by the artifact's `spec_version` and rejects markers it has no schema for.

One thing to know before installing: `chaos.run_experiment` executes real code, so the server is built for trusted local stdio use. The hosted HTTP endpoint at `https://mcp.deepagentlabs.io/mcp` does not execute code — provenance, conformance reports and `chaos.export_aios_run` behave the same there, but experiment runs stay local.

## Requirements
- Needs: Python and uv (commands run via uvx)

## Install

### Claude Code (native platform)

**Platform CLI**
```bash
claude mcp add deep-agentic-core-mcp -- uvx deep-agentic-core-mcp
```
uvx runs the command named after the package; if the README names another one, use uvx --from deep-agentic-core-mcp <command>

### Codex

**Platform CLI**
```bash
codex mcp add deep-agentic-core-mcp -- uvx deep-agentic-core-mcp
```
uvx runs the command named after the package; if the README names another one, use uvx --from deep-agentic-core-mcp <command>

### Cursor

**Config file**
File `.cursor/mcp.json`:
```json
{
    "mcpServers": {
        "deep-agentic-core-mcp": {
            "command": "uvx",
            "args": [
                "deep-agentic-core-mcp"
            ]
        }
    }
}
```
uvx runs the command named after the package; if the README names another one, use uvx --from deep-agentic-core-mcp <command>

### GitHub Copilot

**Config file**
File `.vscode/mcp.json`:
```json
{
    "servers": {
        "deep-agentic-core-mcp": {
            "type": "stdio",
            "command": "uvx",
            "args": [
                "deep-agentic-core-mcp"
            ]
        }
    }
}
```
uvx runs the command named after the package; if the README names another one, use uvx --from deep-agentic-core-mcp <command>

### Gemini CLI

**Platform CLI**
```bash
gemini mcp add deep-agentic-core-mcp uvx deep-agentic-core-mcp
```
uvx runs the command named after the package; if the README names another one, use uvx --from deep-agentic-core-mcp <command>

## When to use it
- profile an agentic workflow and get a structured telemetry summary
- trace a failed run back to the faulty step
- run a controlled chaos experiment against a target workflow
- compare a normal run with a chaos run
- validate an artifact against the AI Operations spec

## Example prompts
- Profile `workflow.json` and show me where the time goes
- Diagnose the trace in `run.json` and name the step that failed
- Run a chaos experiment with the `stream_cut` fault on this workflow

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