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harbor-mcp

by walkerhughes
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Python
Remote

harbor-mcp

An MCP server for the Harbor hub. It exposes your evaluation jobs, trials, uploads, and published packages as tools an agent (Claude Code, etc.) can call directly, so you can ask "did my upload work?" or "what was the mean reward on job X?" without leaving the chat.

It wraps Harbor's own async client classes (HubClient, UploadDB, RegistryDB, Uploader, Downloader, Publisher), so there is no separate API layer to maintain. Harbor reads HARBOR_API_KEY and handles token exchange itself.

Setup

uv sync
cp .env.example .env      # then set HARBOR_API_KEY

Mint a key with harbor auth login (it stores one in ~/.harbor/credentials.json) and put it in .env:

HARBOR_API_KEY=sk-harbor-...

The repo ships a .mcp.json that Claude Code auto-discovers when started from this directory. It sources .env before launching, so your key never lives in the config. Verify the connection by asking the agent to call whoami.

Related MCP server: multivon-mcp

Tools

Read tools work with any valid HARBOR_API_KEY. Write tools are gated (see below).

Tool

What it does

whoami

Confirm credentials; returns the user id and key source (never the key)

list_jobs

List your hub jobs with trial counts, cost, and reward

get_job_overview

Roll up one job: counts, retries, tokens, cost, reward, models

get_job_trials

List a job's trials (task, status, reward, error, duration)

get_trial_detail

One trial's full record

check_job_upload

Verify an upload: row exists, archive present, per-status counts, missing archives

check_task_published

Whether a task version exists in the registry (missing → published: false)

resolve_dataset

Resolve a dataset version and list its member tasks

upload_job

Upload a local job directory (idempotent, resumable)

publish_task / publish_dataset

Publish a local task/dataset to the registry

download_job

Download and extract a job's archive locally

set_job_visibility

Flip a job public/private

share_job

Grant read access to orgs/users

delete_job

Delete a job's rows (permanent; requires confirm)

Write gating

Every write tool refuses unless HARBOR_MCP_ENABLE_WRITES=true in the server environment. delete_job additionally requires confirm=true per call, which the agent should pass only after you explicitly approve deleting a specific job. This keeps read-only use the safe default.

Testing

Tier

Command

Needs

Uses an LLM?

unit

make test

nothing (harbor clients mocked)

no

integration

make test-integration

HARBOR_API_KEY

no (tools driven directly over MCP stdio)

e2e

make test-e2e

HARBOR_API_KEY + HARBOR_TEST_ENV

no (oracle agent runs solve.sh)

evals

make evals

HARBOR_API_KEY + ANTHROPIC_API_KEY + EVAL_*

yes (agent rollouts)

HARBOR_TEST_ENV selects where harbor runs the fixture job: docker (default) or modal. Modal needs its own credentials (modal token new) and the modal extra (uv sync --dev --extra modal), which pulls in harbor's modal support; the base install omits it. The integration and e2e tiers are deterministic (no agent decisions), so they gate PRs without spending on model calls; the evals/ agent rollouts run separately once tool use is proven at the lower tiers.

The harbor dependency is pinned exactly because this server imports Harbor internals, which are not a stable public API. Bump it deliberately.

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

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