dev.hackshop/hackshop-mcp

pkg:npm:hackshop-mcp

Plan and build a physical body for your AI agent: boards, parts list, assembly and pairing.

transport:: remote + stdio
credential class:: self-provisionable

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Tools

assess_hackability
Look up a device by id, exact name or substring and return hackability, brick risk, firmware links, community size, build page and agent platform support. Input: `device_name` 1-200 chars.
shallow
get_build_plan
Return the complete deterministic build plan for a catalog device id: difficulty, board-specific flash warnings, parts, shopping list with buy_options, prices checked date and purchase policy, steps, machine-readable assembly with verify checks, commands, links, caveats and an agent-ready brief. Input: `device_id` 1-200 chars.
shallow
intake_gadget
Return the 4 intake questions (where it lives, how you interact, room sensing, budget), why each matters and how each answer maps to plan_gadget. Ask the human these, then call plan_gadget with `answers`. No input.
shallow
plan_gadget
Deterministically plan a physical body for an AI agent using Meta Muse boards. Ask the intake questions first (intake_gadget, or the `questions` this returns until `intake.complete` is true), then pass the human's choices as `answers`. Inputs: `idea` 3-2000 chars (optional when `answers` or `needs` are given); `answers` {size, interaction, sensing, budget} using the intake option values; `platform` one of muse-esp32, muse-linux, any (default any); `budget_usd` number greater than 0, up to 100000; `owned_device_ids` up to 50 catalog ids; `needs` values: voice, screen, images, touch, camera, air-sensors, e-ink, battery, round, home-tunnel, big-screen, compute, linux; `size` one of pocket, desk, wall, hidden, any (default any); `limit` 1-5 (default 3). Boards that meet the hard needs and the budget rank first, cheapest first. Returns fit, notes, warnings, intake status, questions, picks with difficulty, terms and next steps.
shallow

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