A unified MCP interface for workflow analysis, resilience testing, diagnostics, and agentic system readiness across the DeepAgentLabs ecosystem.
Access the capabilities of the DeepAgentLabs ecosystem through a single MCP server interface.
Analyze agentic workflows and inspect execution evidence, reporting, comparisons, and SLO-oriented summaries.
Run controlled resilience experiments against a sandboxed target using supported fault types.
Inspect Sidecar package status and module inventory to understand current supervision-readiness and runtime state.
Inspect server health, version information, verification status, session state, and MCP server capabilities.
The server provides a shared MCP interface to capabilities exposed by the surrounding DeepAgentLabs packages.
A simple way to understand how an MCP client interacts with the server and receives structured results.
A compatible MCP host sends a request.
The server routes the request to the relevant capability.
Existing package capabilities perform the requested operation.
Results are returned through the MCP interface.
Developers can inspect the resulting workflow evidence.
A representative workflow demonstrating how the MCP server can be used to analyze and test an agentic workflow.
results = {
"request": "Plan a 3-day trip to Goa",
"steps": [
{"name": "flight_search", "result": search_flights()},
{"name": "hotel_search", "result": search_hotels()},
{"name": "weather_check", "result": check_weather()},
{"name": "itinerary_generation", "result": generate_itinerary()},
],
}Simulated workflow used to demonstrate MCP server analysis and resilience-testing capabilities.
Explore how controlled failures can help evaluate workflow resilience using Agentic Chaos capabilities.
Inspect the fault scenarios available for testing.
Execute a selected fault experiment against a test workflow.
Examine the available output and identify possible failure points.
Use the experiment evidence to understand workflow behaviour.
# Illustrative MCP tool sequence
chaos.list_faults()
# Select an appropriate test scenario
# and review its requirements.
chaos.run_experiment(...)
# Inspect the returned experiment results.
# Actual inputs depend on the tool schema.Demonstration concept. Available faults, required inputs, and experiment results depend on the configured MCP server.
Illustrate how workflow analysis, run comparisons, and audit evidence can support an AI release-readiness review.
Review workflow behaviour using available analysis capabilities.
Inspect available comparisons and service-level indicators.
Examine audit information against predefined release criteria.
A team can use the evidence to make a release decision.
# Illustrative evaluation sequence
lens.analyze_workflow(...)
lens.compare_runs(...)
lens.slo_summary(...)
lens.audit_report(...)
# Review the returned evidence against
# your team's release criteria.This is an example evaluation process, not a claim that the MCP server provides a built-in release-gate decision tool.