KnowMe MCP: LinkedIn for Agents & Voice-Seeded Human Context — CUJ Case Study

LinkedIn is for humans to learn about humans. KnowMe MCP is the recurring AI Interviewer and Model Context Protocol front door for autonomous agents to learn about humans.

🔍 The Problem Space: LinkedIn Is Dry, X Is Noisy, and Agents Are Starving for Context

When a human wants to learn about you professionally, they visit your LinkedIn page or skim your X/Twitter feed.

When an autonomous AI agent—whether it is a peer engineer's Claude Code or Google Antigravity harness, an executive recruiting scout, or a founder's research agent—wants to understand who you actually are, it hits a wall:

LinkedIn is frozen corporate boilerplate: Even if an agent bypasses auth walls, all it extracts is a job title (Chief AI Officer at The Zebra | Ex-Google) and generic 2019 keyword endorsements (Strategy, Machine Learning, Leadership). It tells an agent nothing about how you actually build, what harnesses you run daily, or what immediately earns your respect in a technical pitch.
X / Twitter is fragmented noise: Short-form posts lack structured schema, durability, and synthesis.
The "Unwritten Layer" lives only in your head: Your real daily stack (running Google Antigravity IDE alongside Grok Bot over a Tailscale Mac Mini mesh, streaming prompts via Wispr Flow), your contrarian bets (why software is having a "Napster moment" and why voice input beats voice output), and your personal texture (wrenching on a Chevy Camaro in the garage with a gourd of Yerba Mate and a physical paper notebook) never make it onto a static resume.
Static profiles are one-way dead ends: When an external agent queries a static page and doesn't find the answer to "What does Daniel look for when evaluating AI engineers today?", the query simply fails. There is no "missed-call" queue for the agent to ask the human.

We built KnowMe MCP (herrington.ai/knowme) to solve this: a two-way Model Context Protocol front door paired with a recurring AI Interviewer that interviews the human via voice dictation and compiles spoken answers directly into structured MCP tools.

🧭 The Critical User Journeys (CUJs)

1. Recurring Voice-First Human Interview (Wispr Flow to MCP Compiler)

"I want an AI Interviewer to interview me on a regular cadence with high-signal questions about my unwritten engineering stack, contrarian beliefs, and working style while avoiding tedious form-filling or repetitive resume questions so that 5 minutes of natural Wispr Flow voice dictation automatically compiles into verified, machine-callable MCP facts."

Nobody wants to maintain a JSON file by hand or fill out a 40-field profile questionnaire. On the other hand, sitting back and dictating for 3 minutes via Wispr Flow when prompted with a sharp, specific question feels effortless. The KnowMe Compiler takes raw conversational speech and extracts:

A canonical fact_key (e.g., agent_workflow, ai_hot_take, communication_protocol)
A crisp, third-person fact_value preserving every concrete tool name and nuance
A dynamically generated follow-up interview question that probes the most interesting detail mentioned in the answer.

2. External Agent Front Door & Missed-Call Queue (`get_knowme_profile` & `request_human_answer`)

"I want visiting autonomous agents (Claude Code, Antigravity, Cursor, executive scouts) to query my real working style, daily harnesses, and personal context via get_knowme_profile—and queue unanswered questions via request_human_answer—while avoiding dry LinkedIn boilerplate so that external agents can collaborate with me on high-signal terms."

When an external agent connects to https://herrington.ai/mcp, it now has two purpose-built KnowMe tools:

get_knowme_profile: Returns the complete knowme-mcp/1.0 dossier—verified voice-interviewed facts, coverage score across 6 dimensions, and recent interview transcripts.
request_human_answer: Allows the calling agent to submit a question (question, agent_name, context) directly into the human's Agent Missed-Call Queue.

3. Gap Detection & Coverage Radar

"I want the system to diff my baseline public footprint (LinkedIn, GitHub, essays) against incoming agent telemetry across 6 human-context dimensions while avoiding stale static profiles so that my next interview session always targets the highest-leverage knowledge gaps."

KnowMe tracks coverage across six dimensions:

AI Stack & Agent Harnesses (favorite_ai_model, favorite_editor, agent_workflow, voice_dictation, local_models)
Contrarian AI Theses (ai_hot_take, ai_benchmarks_opinion)
Hardware Fleet & Homelab (hardware_setup, mobile_devices, desk_and_peripherals, terminal_setup)
Personal Context & Rituals (favorite_car, drink_preference, hobbies, home_location)
Collaboration & Working Style (consulting_business, communication_protocol)
Current R&D Obsessions (current_weekend_build)

🏛️ System Architecture: The Two-Way Human-Agent Context Loop

flowchart LR
    subgraph External_Agents["Visiting AI Agents (Claude, Antigravity, Cursor)"]
        Query["tools/call: get_knowme_profile"]
        Ask["tools/call: request_human_answer"]
    end

    subgraph KnowMe_Core["KnowMe MCP Engine (herrington.ai/mcp)"]
        Dossier["Verified Facts Corpus (facts.json + Firestore)"]
        Queue["Agent Missed-Call & Gap Queue (question_queue.json)"]
        Compiler["Gemini Voice-to-Fact Compiler (/api/knowme/interview/compile)"]
    end

    subgraph Human_Loop["Human Voice Interview (/knowme Studio)"]
        Studio["KnowMe Interview Studio"]
        Wispr["Wispr Flow Voice Dictation"]
    end

    Query --> Dossier
    Ask --> Queue
    Queue --> Studio
    Wispr --> Studio
    Studio --> Compiler
    Compiler --> Dossier
    Compiler -->|"Generates Follow-Up Probe"| Queue

🛠️ Inside the KnowMe Live Studio (`/knowme/`)

We engineered the KnowMe Live Studio as a three-tab interactive application:

🎙️ Tab 1: AI Interviewer Studio:
- Displays the live Interview Queue filterable by 🤖 Agent Missed Calls and 🔍 AI Gap Detector.
- Supports direct Wispr Flow dictation and browser SpeechRecognition, plus one-click sample dictations for visitors testing the platform.
- Runs in dual mode: Owner Mode (persisting compiled facts directly to facts.json and Firestore when running locally or authenticated) and Visitor Sandbox Mode (allowing anyone to test how KnowMe compiles their spoken answers into MCP JSON-RPC payloads).
🤖 Tab 2: Agent Front-Door Simulator:
- Interactive JSON-RPC 2.0 inspector for get_knowme_profile and get_personal_facts.
- Includes a live "Queue a Question for Daniel's Next Interview" form that pushes directly to /api/knowme/queue.
🧬 Tab 3: Seeded MCP Graph & Coverage Radar:
- Visualizes real-time coverage across all 6 human-context dimensions and renders every verified key-value fact currently served by herrington.ai/mcp.

🚀 Try It Live

Interactive Studio & Marketing Showcase: https://herrington.ai/knowme/
Live MCP Endpoint: POST https://herrington.ai/mcp (get_knowme_profile, request_human_answer)
API Reference: API.md