Build intent specifications through guided Socratic conversations with AI, then connect to your Intent Layer for workspace context and dependency insights. For developers designing intent-driven systems.
io.github.pathmodeio/mcp-server
Local install
STDIO
No auth required
How models use it and what it is built for.
Build intent specifications through guided Socratic conversations with AI, then connect to your Intent Layer for workspace context and dependency insights. For developers designing intent-driven systems.
Local install — runs as a subprocess.
Configuration this server reads at startup.
Optional: Your Pathmode API key for team features (dependency graph, workspace context). Get one from Settings > API Keys in the Pathmode app.
Where to find authoritative docs and source for Pathmodeio.
Paste any of these into Agent Studio after connecting Pathmodeio.
Common questions about connecting and running Pathmodeio.
What is an intent spec and why would I build one?
An intent spec formally describes what you want your system to do. This server uses Socratic dialogue to help you think through and articulate intents clearly before implementation, reducing ambiguity and rework.
Do I need a Pathmode API key to use this server?
No, the PATHMODE_API_KEY is optional. Without it, you get basic Socratic intent-building. With a key, you unlock team features like dependency graphs and workspace context to inform your specs.
How do I set up the Pathmode API key?
Set the PATHMODE_API_KEY environment variable before running the server. You can obtain a key from Pathmode's team features portal. The server will use it to fetch workspace and dependency context automatically.
Can I use this without connecting to an Intent Layer?
Yes. The server can guide you through intent spec design via conversation alone. Connecting an Intent Layer (via the API key) adds workspace awareness and dependency visibility, but is not required.
What does 'Socratic AI conversation' mean in this context?
The server asks clarifying questions to help you think through your intent deeply, rather than just accepting your first description. This iterative questioning helps surface hidden assumptions and edge cases in your spec.
MCP Playground runs 10,000+ hosted MCP servers — GitHub, Linear, Notion, Stripe, Sentry and more — across Claude, GPT, Gemini, DeepSeek and 30+ AI models. Compare model answers side-by-side, save agent presets, share runs. Zero install.
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