AI Workbench MCP server

AI Workbench

<!-- mcp-name: io.github.hrishikesh-thakre/ai-workbench-mcp -->

AI Workbench supervises AI coding agents, captures evidence, validates work, applies acceptance policy, and produces auditable PR-ready reports.

The PyPI package remains ai-workbench-mcp for this public alpha because the ai-workbench package name is already occupied. The product and CLI are AI Workbench:

pip install ai-workbench-mcp
ai-workbench --help

Current source metadata targets unpublished ai-workbench-mcp==0.8.0a0. This public alpha consolidates local supervision, evidence capture, validation, acceptance policy, and PR reporting into one product surface.

Public Alpha Warning

The supervisor is the preferred automated evidence path, but daemon, Codex hook, and OpenCode adapter coverage are alpha mechanisms. AI Workbench checks evidence quality and acceptance readiness; it does not prove the work is absolutely correct. High-risk work still requires human review.

Architecture

• AI Workbench supervisor captures local evidence.
• AI Workbench validation writes validation_report.json.
• AI Workbench quality gate writes revision_decision.json.
• AI Workbench PR/report surfaces render accept, needs_review, or block.

Agent output is a proposal. Workbench accepts evidence.

MCP is the connection protocol. AI Workbench MCP is the tool server. Acceptance is decided by the selected validation profile and quality gate. The agent performs. Workbench accepts. MCP connects them.

Quick Start

Register a project once and start the local supervisor:

pip install ai-workbench-mcp
ai-workbench supervisor setup --project-dir . --task-type code_change
ai-workbench supervisor start

Run Codex, OpenCode, Goose, or another supported local workflow in the project. Then inspect the latest report:

ai-workbench supervisor status
ai-workbench reports show latest --project-dir .

Render PR-ready artifacts from a finalized run:

ai-workbench pr-gate --run-dir runs/<run_id>

The canonical local run ledger is:

runs/<run_id>/
  task_metadata.json
  final_prompt.md
  model_selection.json
  model_output.md
  validation_report.json
  revision_decision.json
  run_log.jsonl
  metadata.json
  transcript.jsonl
  commands.jsonl
  workspace/
  validation/
  artifacts/

validationreport.json and revisiondecision.json are the final acceptance authority. Supporting supervisor reports are local evidence, not a substitute for those Workbench artifacts.

Codex Hooks

Install project-local Codex hooks:

ai-workbench setup codex --project-dir . --task-type code_change

Restart Codex or start a new session, open /hooks, review the project hook, and trust it once. Until a hook event is observed, supervisor status reports Codex coverage as configured but unverified.

Goose MCP

AI Workbench still exposes the same MCP tool lifecycle. Register the server with Goose or another MCP host using:

ai-workbench mcp serve

The seven MCP tools remain:

workbench_open_run
workbench_select_policy_pack
workbench_select_model
workbench_record_execution
workbench_validate_run
workbench_quality_gate
workbench_analyze_runs

PR Gate

Workbench PR acceptance consumes real Workbench run evidence:

ai-workbench pr-gate \
  --run-dir runs/<run_id> \
  --out runs/pr_gate/pr_comment.md \
  --json-out runs/pr_gate/pr_decision.json

Outcomes are exactly:

• accept
• needs_review
• block

Missing, unreadable, or scaffold-only evidence blocks. A green CI run, uploaded artifact, sticky PR comment, or model self-claim is not acceptance evidence.

Bootstrap Assets

To add starter configs, prompts, recipes, docs, and the GitHub PR-gate workflow to a repository:

ai-workbench bootstrap --target .

The bootstrap keeps runs/ ignored.

Package Demo

Shortened. The full README is on GitHub.