Give Claude Your Full Product Context (Without Copy-Pasting)

Every time you paste your product vision into a Claude conversation, you lose something. The strategy doc gets trimmed. The OKR context is missing. The discovery findings from last month never make it in. You end up re-explaining your product to an AI that could already know it.

The Problem With Copy-Paste Context

Product teams have rich context spread across multiple documents: vision statements, strategy canvases, OKR hierarchies, discovery findings, product ideas, agent specifications. Pasting fragments into chat windows is lossy. The AI gets a partial picture and gives partial answers.

Delvyn Studio solves this with a built-in MCP server. MCP (Model Context Protocol) lets Claude connect directly to your product data - all of it, live, structured, and always current.

What Claude Gets Access To

Once connected, Claude can query 19 tools across your entire product context:

• Product vision and strategy - Your full vision statement, strategic priorities, market analysis, and guardrails (the things you are deliberately not doing)
• OKRs and progress - Active cycle objectives, key result progress, check-in history, team alignment
• Discovery and ideas - Product ideas with status and impact scores, discovery experiments, research findings, risk assessments
• Agent specifications - The structured specs that tell AI coding agents what to build and why
• Learning captures - Retrospective insights from past implementations, including spec quality ratings

Three Steps to Connect

Setup takes under five minutes:

• Generate an API key - Go to Settings → API Access in Delvyn Studio. Create a key, name it (e.g., "Claude Code"), and copy it.
• Add the config - Open your Claude Desktop config or create a .vscode/mcp.json file and add Delvyn Studio as a server.
• Ask away - Claude now has structured access to your product thinking. Ask it anything about your strategy, goals, or learnings.

For Claude Desktop, add this to your configuration:

{ "mcpServers": { "delvyn": { "url": "https://api.delvynstudio.com/mcp", "headers": { "Authorization": "Bearer YOUR_API_KEY" } } } }

Same format works for Claude Code (.vscode/mcp.json) and Cursor (.cursor/mcp.json). Full client setup details in the MCP documentation.

What You Can Ask

The range is broad because your product context is broad:

• "What does our product vision say about data privacy?"
• "What are our strategic guardrails for this quarter?"
• "Show me discovery findings related to onboarding friction"
• "What did we learn from the last implementation of the checkout flow?"
• "Find agent specs related to the notification system"
• "Which product ideas have high impact but haven't been prioritized?"
• "What key results are at risk in our current cycle?"

Claude answers from your real data. Not generic product management advice - your vision, your strategy, your learnings.

How Turbine Studios Uses This for Feature Specs

Turbine Studios is a 15-person B2B SaaS startup. Their product manager, James, writes feature specifications every sprint. Before MCP, that meant opening four tabs: vision doc, strategy canvas, recent discovery findings, and the OKR dashboard. He would paste relevant fragments into Claude and ask for help structuring the spec.

“I stopped digging through old docs. Now I ask Claude 'what does our strategy say about patient data handling?' and 'what did we learn from the last onboarding discovery?' - and it answers from our actual Delvyn data. My last spec took 90 minutes instead of half a day.”

James also uses the learning captures tool. After each feature ships, he rates the spec quality and notes what surprised him. Those learnings surface automatically when he asks Claude about similar features later. Context compounds over time.

Why This Matters for AI-Native Teams

AI coding agents like Claude Code are only as good as the context they receive. CLAUDE.md tells them how to build. Agent specs tell them what to build. But without live access to your strategic context, specs start from scratch every time.

With MCP connected, Claude can pull your vision, check your guardrails, reference past learnings, and validate that a feature aligns with your OKRs - all without you pasting a single document. The product thinking layer becomes ambient context, not manual input.