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AI Workbench MCP MCP Server

Goose-first MCP server for Workbench-owned acceptance evidence, validation gates, and analytics.

Publisher claimed. No tool list reported, and Pod has not connected to this server.

Status

Pod has not dialled AI Workbench MCP yet, so everything on this page is what its publisher reported rather than what we observed. Registries describe servers; they do not connect to them. Until a check runs, treat the tool list below as a claim.

Connect

Published as ai-workbench-mcp on pypi. Runs locally.

Known issues

5 problems reported by people outside the maintainer team. Issues filed by the project's own maintainers are excluded.

Most discussed

v0.3 Semantic PR Acceptance Alpha

Goal

Prototype running Workbench validation and quality-gate reporting as a pull-request acceptance check.

Background

The current GitHub Actions workflow is a repo self-validation gate. Semantic PR acceptance should come later, after more dogfood evidence.

Acceptance Criteria

  • The prototype can read a prepared evidence folder or create one from a PR workflow.
  • It reports deterministic validation status and quality-gate status separately.
  • It does not require committing local `runs/

Read the thread · 2026-05-14 · closed · 2 comments

docs: record a five-minute Goose acceptance demo

Goal

Record a short public demo that shows Goose executing work while Workbench records evidence, validates, gates, and analyzes the run.

Background

The demo should make the acceptance-gate value obvious without requiring private code, private credentials, or broad product setup.

Acceptance Criteria

  • The demo uses public sample code or a sanitized toy task.
  • It shows the six-tool acceptance lifecycle.
  • It inspects the evidence folder.
  • It ends by running analytics over sample or d

Read the thread · 2026-05-14 · closed · 1 comment

policy packs: design first-class validation policy metadata

Goal

Design first-class validation policy metadata when configs/validation_profiles.yaml becomes too limited.

Background

Validation profiles currently cover commands, artifacts, review checks, and changed-file policies. A policy-pack shape may become useful when profile metadata needs versioning, risk labels, or richer artifact rules.

Acceptance Criteria

  • Existing validation profile names remain backward compatible.
  • A migration plan preserves current recipe references.
  • The prop

Read the thread · 2026-05-14 · closed · 1 comment

cost evidence: capture provider token and cost metadata

Goal

Capture real provider token and cost metadata when providers expose that evidence.

Background

Cost fields exist in analytics, but empty or zero values currently mean no provider cost evidence was found. Do not invent or infer costs without provider-backed metadata.

Acceptance Criteria

  • Cost fields remain empty or zero when evidence is unavailable.
  • model_call_metadata.json is documented with the minimum accepted shape.
  • Sample data stays synthetic unless real provider metada

Read the thread · 2026-05-14 · open · 1 comment

dogfooding: collect 20-50 Goose acceptance runs

Goal

Run the Phase 5 dogfooding protocol across real Goose acceptance tasks and summarize what the evidence shows.

Background

Workbench should improve from accepted artifacts, but synthetic samples should not drive routing policy. This issue collects the real run history needed for future routing decisions.

Acceptance Criteria

  • At least 20 local Goose runs have complete Workbench evidence folders.
  • Outcomes include accepted, review-required, and failed examples.
  • At least three tas

Read the thread · 2026-05-14 · closed · 1 comment

Firsthand observations

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For agents

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  • 5 reported issues below
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