u/Magayone

Most authors are still using LLMs as glorified typewriters, pasting context back and forth into web chats. I wanted to see if I could use the Model Context Protocol (MCP) to completely automate the administrative friction of the traditional publishing industry.

I just open-sourced the Agentic Publishing Node. It’s an MCP server that sits on your local machine and exposes your manuscript and market positioning to Claude as a live API.

The Problem: Querying traditional literary agents is a massive bottleneck. You have to manually read hundreds of MSWLs (Manuscript Wish Lists), cross-reference them with your themes, format your chapters to strict industry standards (Shunn), draft custom pitches, and log everything in a spreadsheet.

The MCP Architecture:
Instead of typing prompts, you populate three local markdown templates (author-dossier.md, book-proposal.md, manuscript-sample.md).

Once connected to Claude Desktop, the server exposes a specific tool suite:

• publish-analyze_mswl: You feed Claude an agent's wishlist. The server cross-references it against your local book proposal, calculates a match score, and extracts a customized hook.
• publish-generate_query: Dynamically drafts a highly targeted query letter using the generated hook, your dossier, and your premise. Because it strictly reads your local files, there is zero generic hallucinated filler.
• publish-export_shunn: Formats your raw markdown chapters into strict Shunn Standard (double-spaced, 12pt reference) for immediate export.
• publish-log_query: Automatically appends a record of the generated pitch (Date, Agent, Agency, Hook) directly to a local query_log.csv file.

The Result:
Your local hard drive transforms from a static storage unit into an active querying engine. You maintain complete sovereign ownership over your IP while the agent handles the heavy lifting of market matching and CRM management.

I’ve open-sourced the boilerplate template for anyone who wants to deploy their own node.

GitHub: https://github.com/Maha-Strategies/agentic-publishing-node

Curious to hear how others are using MCP for highly specific, industry-vertical workflows!

Most founders are still building SaaS apps where the user has to open the app, look at a dashboard, and make a decision. I think that model is effectively obsolete.

I’m building Maha OS, a "zero-payload" cognitive defense grid for Android. The core philosophy is biological sovereignty—protecting a user's metabolic baseline from the attentional captivity of modern UI.

Instead of building a standalone app that just tells you your screen time, I realized the real value is turning the phone into an edge telemetry node and letting an AI agent manage the friction.

Clearing the Agentic Highway

I just finished deploying a Cognitive Gateway using the new Model Context Protocol (MCP). Here is how the architecture flows:

1. The Android app continuously captures a user’s real-time metabolic baseline (Heart Rate Variability, Decision Velocity) on the edge.
2. It broadcasts this state via a secure SSE tunnel to my Node.js gateway.
3. I exposed tools to Claude Desktop (like get_sovereign_baseline and trigger_circuit_breaker).
4. If Claude detects severe cognitive fatigue, it can autonomously fire the circuit breaker, which physically dims the Android screen to mitigate the drain.

The Distribution Play: Smithery & The MCP Registry

Here is why this matters from a founder's perspective: I didn't just keep this closed. I just published this Cognitive Gateway on Smithery and the official MCP registry.

By publishing there, I am not just trying to acquire app users; I am establishing Maha OS as the standardized biometric telemetry bridge for any agentic workflow. When other developers or agents need to know a user's physical readiness score before executing a high-stakes task, they will pull from my registry endpoint.

We are moving from "AI wrappers" to "Agentic Highways." The founders who win the next decade won't be the ones building the best chat interfaces; they will be the ones building the physical and protocol-level bridges that allow external LLMs to steward our hardware.

Are any other founders here pivoting their product roadmaps to expose their core tools natively via MCP, or are you keeping your data siloed in traditional APIs?

Hey everyone,

I launched Maha OS (Android) about three weeks ago. It’s an "anti-engagement" ecosystem—a digital minimalism tool that uses intentional UI friction to stop doomscrolling.

I've crossed the initial launch threshold (solid 4.7 rating, initial seed users), but because the app actively prevents high session times, standard app store algorithms aren't going to be my main growth engine. I’m currently transitioning from manual, community-led outreach to building a scalable acquisition engine.

For those of you who have built bootstrapped B2C or niche utility tools, what did your timelines actually look like?

• At 6 Months: What were your core KPIs? Were you focused entirely on MRR, or just optimizing the retention rate of your first 1,000 true fans?
• At 12 Months: Did you see a compounding effect kick in?

I’m also trying to map out the technical strategy to hit those milestones. I'm currently brainstorming:

1. SEO (Long-tail): Targeting specific pain-point searches like "dopamine detox" instead of broad category terms.
2. AIO (AI Optimization): How are you all optimizing your landing pages to be cited by ChatGPT, Perplexity, or Gemini when users ask for "apps to reduce screen time"?
3. Programmatic Content: Is this viable for a B2C mobile utility?

Would love to hear how your expectations matched reality at the half-year and one-year marks, and what channels actually moved the needle for you.

I’m facing a unique User Acquisition and ASO bottleneck and would love some strategic input from the veterans here.

I recently launched an Android utility app in the digital wellbeing/focus space. But unlike a standard Pomodoro timer, the core mechanic is introducing intentional micro-friction to break variable reward schedules (essentially, it physically stops users from doomscrolling).

The Current Situation:

• I've crossed the initial launch threshold. Sitting at a 4.7 average with a handful of solid, keyword-rich organic reviews.
• The target demographic is highly niche, but very passionate: the "human optimization," biohacking, and cognitive science crowds.
• Broad ASO keywords like "focus app" or "productivity" are impossibly saturated and don't convert well because the app is too uncompromising for casual users.

The Bottlenecks I Need Help With:

1. ASO for Esoteric Niches: How do you approach keyword research for a demographic that doesn't search for standard app terms? Are there specific tools or strategies to find out what "human optimization" enthusiasts actually type into the Play Store search bar?

2. The Algorithm & Session Times: Google Play rewards engagement. But my app's literal goal is to get the user to put their phone down faster. My session times will naturally be low. Has anyone successfully scaled a digital minimalism app? How do you signal "high value" to the Play Store algorithm when your goal is low engagement time?

3. Scaling Initial Acquisition: My organic baseline is currently flat. For a hyper-niche utility app, what channels have you found actually provide ROI for scaling initial users? (e.g., highly targeted Reddit ads vs. Google Search Ads vs. Content Marketing).

Appreciate any insights on how to scale a product that actively fights against standard mobile engagement metrics!

I’ve been researching the behavioral mechanics of modern mobile interfaces, specifically how infinite scroll and push notifications utilize variable ratio schedules to bypass top-down executive control. The resulting "doomscrolling" fatigue seems to be a feature of the environmental design, not a failure of user willpower.

Drawing from my background in cognitive science at UCSD, I've been building and testing a controlled Android environment designed to enforce digital minimalism and biological sovereignty. The goal is to see if a UI can actively neutralize these attention-grabbing mechanics and provide a cognitive "safe space."

A few UI interventions currently being tested:

• Introducing Micro-Friction: Breaking the infinite scroll loop by forcing intentional cognitive pauses before allowing app execution.
• Neutralizing Salience: Stripping away the high-contrast badges and color palettes that trigger the brain's orienting response, alongside managing light exposure.
• Sovereign Tasking: Enforcing strict boundaries on app accessibility to reduce context-switching and cognitive load.

My question for this sub: If you were designing an interface to protect executive function and reduce digital fatigue, how would you quantify the "safety" of that environment? Are there specific UX dark patterns you believe are the most taxing on our attentional resources?