Racksmith: The Autonomous Modular Synth Planner Powered by Sanity Context MCP

A developer built Racksmith, an autonomous modular synthesizer planner that pairs a Gemini 3.8 Flash agent via the Vercel AI SDK with a Sanity Knowledge Lake through a Model Context Protocol (MCP) server. The agent queries structured module documents with field-level provenance, runs a deterministic mathematical validation engine for power, HP width and mechanical depth checks, and surfaces manufacturer errata in a photorealistic 3D hardware visualizer.

This is a submission for the Sanity Challenge, Path One: Ship an Agent That Queries Real Content https://dev.to/challenges/sanity-2026-09-16 r674mqrk Dataset: production Building a Eurorack modular synthesizer is notoriously treacherous. Beginners and professional sound designers alike routinely damage expensive hardware due to four silent traps: +12V , -12V , and +5V . When powering on, analog oscillators and digital DSP modules draw an inrush surge exceeding 150–200% of steady-state draw. Exceeding Racksmith solves this by pairing an autonomous AI agent powered by the Vercel AI SDK and Gemini 3.8 Flash with a Sanity Knowledge Lake via a standards-compliant Model Context Protocol MCP server. The agent queries structured documents with field-level provenance, evaluates safety through a deterministic mathematical validation engine , surfaces real-world manufacturer errata in an interactive resolution modal, and renders the result in a photorealistic 3D interactive hardware visualizer . +------------------------------------------------+ | User Browser Client | | Next.js 16 + React Three Fiber 3D + Zustand | +-----------------------+------------------------+ | +-------------------+-------------------+ | User Prompt / GUI Actions | v v +--------------------+ +---------------------+ | AI Agent Router | | 5-Step Golden Path | | Vercel AI SDK + | | Demo Controller | | Gemini 3.8 Flash | +----------+----------+ +---------+----------+ | | Tool Calls | v | +-------------------------------------+ | | Model Context Protocol MCP Client | | | - searchSanityKnowledge | | | - getModule / getCase | | | - getContradictions | | | - validateRackDeterministic | | | - saveUserDecision | | +-----------------+-------------------+ | | Linked Transport | v | +-------------------------------------+ | | Sanity Context MCP Server | | | Model Context Protocol Spec 1.2.0 | | +-----------------+-------------------+ | | GROQ / Lake Fetch | v | +-------------------------------------+ | | Sanity Lake r674mqrk | | | - 33 Modules | | | - 4 Cases | | | - 8 Manufacturers | | | - 6 Claims with Provenance | | | - 3 Contradictions with Errata | | | - UserDecisions Persisted | | +-----------------+-------------------+ | | | +---------------+---------------+ | v +---------------------------------------+ | Deterministic Validation Engine | | - HP Width Boundary Check | | - Mechanical Depth Collision Check | | - 3-Rail Power & 80% Headroom Buffer | +-------------------+-------------------+ | v +---------------------------------------+ | Photorealistic 3D Eurorack Rack | | Anodized Faceplates, Jacks, Collide | +---------------------------------------+ In this demonstration, the AI Agent plans a modular synth system autonomously using natural language while querying real content from Sanity: The user navigates to the AI Agent tab. The agent is initialized with direct tool bindings to the Sanity Context MCP server. The user enters a complex hardware request: "Build an ambient sound design rack with complex modulation and reverb, make sure modules fit in depth and don't exceed power limits." The agent executes tool calls against the Sanity MCP Server: searchSanityKnowledge : Queries Sanity for ambient sound sources and filters. getModule : Retrieves exact dimensions and multi-rail power draws. validateRackDeterministic : Passes candidate configurations through the mathematical engine, automatically rejecting modules that exceed case depth or violate the 80% power headroom ceiling. The agent presents its verified reasoning, citing the exact GROQ documents retrieved from Sanity, and directly mounts the optimal modules Plaits, Rings, Beads, Maths SMD onto the 3D hardware rack without human intervention. The complete source code is public and open source on GitHub: 👉 https://github.com/Shreyansh00987/Racksmith https://github.com/Shreyansh00987/Racksmith ai/rsc + Google Gemini 3.8 Flash If Sanity were replaced with a generic vector database, Racksmith would fail . A modular synthesizer build requires strict relational invariants: module.depthMM + clearanceBuffer <= case.maxDepthMM sum module.powerPlus12 <= case.powerCapacityPlus12 0.80 In an unstructured vector store, a search for "Make Noise Maths power draw" returns chunk embeddings where "Draws 60mA" and "Draws 90mA under active cycle" look like identical high-confidence semantic matches. An LLM has no mechanism to determine which claim corresponds to which revision or test methodology. With Sanity, specifications are stored as typed, structured entities with field-level provenance . module hp , mechanical depth depthMM , 3-rail current powerPlus12 , powerMinus12 , powerPlus5 , category, and manufacturer reference. case maxDepthMM , and power supply ratings manufacturer claim field , value , unit , sourceURL , revision , confidence . contradiction claimA <- claimB with explanation , conflictType , and impactAnalysis . userDecision The Next.js application exposes an MCP server /api/mcp implementing 5 core tools: // Example: Sanity MCP Tool for Discrepancy & Errata Retrieval server.tool 'getContradictions', 'Retrieve known specification contradictions and manufacturer errata', { moduleId: z.string .optional }, async { moduleId } = { const query = moduleId ? type == "contradiction" && claimA- module. ref == $moduleId || claimB- module. ref == $moduleId { id, title, explanation, impactAnalysis, claimA- { field, value, unit, source, revision }, claimB- { field, value, unit, source, revision } } : type == "contradiction" { id, title, explanation, impactAnalysis, claimA- { field, value, unit, source, revision }, claimB- { field, value, unit, source, revision } } ; const result = await sanityClient.fetch query, { moduleId } ; return { content: { type: 'text', text: JSON.stringify result, null, 2 } }; } ; The agent does not guess math. When an agent wants to evaluate a rack configuration, it calls the validateRackDeterministic MCP tool: export function validateRack modules: Module , targetCase: Case : ValidationResult { const totalHp = modules.reduce acc, m = acc + m.hp, 0 ; const hpOverflow = totalHp targetCase.totalHp; const depthCollisions = modules .filter m = m.depthMM targetCase.maxDepthMM .map m = { module: m.name, moduleDepth: m.depthMM, maxDepth: targetCase.maxDepthMM } ; const currentPlus12 = modules.reduce acc, m = acc + m.powerPlus12, 0 ; const maxPlus12Safe = targetCase.powerCapacityPlus12 0.80; const power12Overload = currentPlus12 maxPlus12Safe; return { isValid: hpOverflow && depthCollisions.length === 0 && power12Overload, metrics: { totalHp, currentPlus12, maxPlus12Safe }, depthCollisions, hpOverflow, power12Overload }; } https://racksmith.vercel.app , http://localhost:3000 https://r674mqrk.api.sanity.io/v2024-01-01/data/query/production?query= type=="module" 0...5 {name,hp,depthMM,powerPlus12} Below is a curated transcript excerpt demonstrating how the agent invokes Sanity MCP tools to safely resolve an ambiguous hardware request: { "role": "user", "content": "Build an ambient sound design rack with complex modulation and reverb, make sure modules fit in depth and don't exceed power limits." }, { "role": "assistant", "thought": "The user wants an ambient rack. I must query Sanity Knowledge Lake for modules tagged with 'ambient', 'reverb', or 'modulation'. I will check their depths against standard case clearances.", "tool call": { "name": "searchSanityKnowledge", "arguments": { "query": "ambient modulation reverb granular" } } }, { "role": "tool", "name": "searchSanityKnowledge", "content": { "name": "Mutable Instruments Beads", "category": "granular", "hp": 14, "depthMM": 25, "powerPlus12": 100 }, { "name": "Mutable Instruments Rings", "category": "resonator", "hp": 14, "depthMM": 25, "powerPlus12": 120 }, { "name": "Make Noise Maths", "category": "modulation", "hp": 20, "depthMM": 25, "powerPlus12": 90 } }, { "role": "assistant", "thought": "I will check for known contradictions on Make Noise Maths before adding it.", "tool call": { "name": "getContradictions", "arguments": { "moduleId": "module-make-noise-maths" } } }, { "role": "tool", "name": "getContradictions", "content": { "title": "Make Noise Maths Power & Depth Errata", "explanation": "Vintage THT units draw 60mA quiescent and measure 45mm deep. Modern SMD units draw up to 90mA active cycle and measure 25mm deep.", "impactAnalysis": "Vintage revision will collide with shallow skiff cases Palette 62 ." } }, { "role": "assistant", "thought": "Selecting the Modern SMD revision guarantees 25mm depth, safely fitting within the 45.5mm skiff limit. Now validating total power.", "tool call": { "name": "validateRackDeterministic", "arguments": { "caseId": "case-palette-62", "moduleIds": "module-plaits", "module-rings", "module-beads", "module-maths-smd" } } }, { "role": "tool", "name": "validateRackDeterministic", "content": { "isValid": true, "totalHp": 60, "maxHp": 62, "powerPlus12": 360, "maxPowerPlus12Allowed": 960, "depthCollision": null } }, { "role": "assistant", "content": "I have assembled an ambient sound design rack for you I verified through the Sanity Knowledge Lake that Make Noise Maths has a known errata between vintage THT 45mm and modern SMD 25mm . By selecting the modern SMD revision, all 4 modules Plaits, Rings, Beads, and Maths safely fit into your 62HP case with 0 depth collisions and consume only 360mA on the +12V rail well below your 80% safety threshold of 960mA ." } Built with ❤️ for the DEV & Sanity Community by Shreyansh @Shreyansh00987 https://github.com/Shreyansh00987