Delivery Intelligence MCP Workbench

A governed AI/MCP delivery-intelligence showcase over a fully synthetic enterprise programme.

It helps a delivery lead answer: what changed, what is blocked, which dependency matters next, what a change request will affect, and which claims are supported by evidence. The core value works without an API key.

60-Second Review Path

1. Open the generated workbench: workbench/index.html
2. Inspect the strongest code path: delivery_intelligence/engine.py
3. Review the MCP tool boundary: delivery_intelligence/mcp_server.py
4. Check the evaluation harness: delivery_intelligence/evaluation.py
5. Run the project:

python -m pip install -e .
python -m delivery_intelligence build
python -m delivery_intelligence validate
python -m unittest discover -s tests

What This Proves

Capability	Public evidence
AI/MCP tool design	Seven focused MCP tools over a coherent programme model, with structured outputs and validated arguments.
Delivery/programme thinking	Milestones, RAID, decisions, change requests, readiness gates, dependencies and steering context pack.
Hallucination controls	Unsupported claims return insufficient_evidence instead of becoming facts.
Evidence traceability	Outputs separate source facts, deterministic derivations and recommendations with evidence references.
Evaluation discipline	Deterministic checks cover tool contracts, evidence coverage, refusal behavior, change impact and repeatability.
Cost/latency awareness	Tool outputs include latency and estimated-cost telemetry; deterministic core has zero model cost.

Architecture

flowchart LR
    A[Synthetic programme fixture] --> B[Deterministic delivery engine]
    B --> C[Evidence ledger]
    B --> D[Tool registry]
    D --> E[Optional MCP server]
    D --> F[Evaluation harness]
    D --> G[Generated workbench]
    H[Future data adapter] -. documented boundary .-> A
    I[Optional narrative adapter] -. recommendations only .-> D

The public showcase stands alone. It does not import another portfolio repo and does not expose private operational, job-search, email, salary, eligibility, credential or account data.

Tool Surface

Tool	Purpose
get_program_health	Explainable health score with formula, limitations and evidence.
list_priority_risks	Deterministic RAID prioritisation by severity, probability, impact and overdue status.
trace_dependency_impact	Downstream dependency traversal from a milestone.
assess_change_request	Schedule, cost, scope and readiness impact for a change request.
get_blocked_decisions	Blocked decisions, blockers and missing evidence.
build_steering_context_pack	Board-ready context pack with facts, derivations and recommendations separated.
get_evidence_for_claim	Evidence lookup or refusal when support is insufficient.

Facts vs Derivations vs Recommendations

Layer	Meaning	Example
Source facts	Synthetic fixture records: milestones, RAID, dependencies, decisions, gates and snapshots.	Payment freeze forecast moved to day 92.
Deterministic derivations	Python-calculated scores, risk ranks, impact paths and evidence coverage.	Payment freeze slippage propagates to pilot launch.
Recommendations	Policy suggestions generated from traceable facts and derivations.	Split or defer a change request unless sponsor accepts schedule impact.
Optional AI narrative	Future adapter boundary only; not required for tests or demo.	A model may rewrite a steering summary, but cannot create unsupported facts.

MCP Usage

The core package has no runtime dependencies. To run the real MCP server using the official Python SDK:

python -m pip install -e ".[mcp]"
delivery-intelligence-mcp

The MCP extra is optional because the deterministic engine, workbench and evaluation harness should remain reviewable without service credentials or model access.

Evaluation Results

Run:

python -m delivery_intelligence eval

The harness checks:

• tool-call contract validity;
• evidence coverage for health and steering outputs;
• unsupported-claim refusal;
• change/dependency impact correctness against the known fixture;
• repeatability across deterministic runs;
• latency and estimated-cost telemetry presence.

Safety Boundaries

• Synthetic programme only.
• No external actions.
• No broad file-system, network or shell tools.
• No vector database or orchestration framework.
• No API key required.
• No private programme, job-search, resume, recruiter, account, salary, legal, eligibility or credential data.
• Future adapters must preserve the same evidence boundary.

Repository Layout

delivery_intelligence/
  fixtures.py       synthetic programme records
  engine.py         health, risk, dependency, change and evidence reasoning
  tools.py          validated tool registry and telemetry wrapper
  mcp_server.py     optional real MCP server using the Python SDK
  evaluation.py     deterministic evaluation harness
  workbench.py      generated HTML/SVG workbench artifacts
tests/              engine, tool, evaluation and visual-output checks
docs/               architecture, tool, evaluation and portfolio notes
workbench/          generated self-contained workbench

License

MIT.