AGNTCon + MCPCon Europe 2026 — Intelligence Hub & Native MCP Server

An independent, fair-use research wiki, interactive AI assistant, and Model Context Protocol (MCP) server for AGNTCon + MCPCon Europe 2026 (17–18 September 2026, RAI Amsterdam).

Academic & Community Fair-Use Notice:
This is an unofficial, independent open-source research and intelligence project. Original conference presentations, schedules, and materials are copyright © The Linux Foundation / Agentic AI Foundation and their respective speakers. This repository contains original analytical syntheses, verified open-source tool repositories, and outbound links to official Sched presentation pages. No speaker slides are reproduced or hosted.

🏛️ System Architecture

┌─────────────────────────────────────────────────────────────────────────────┐
│                       HARVESTER & EXTRACTION PIPELINE                       │
│  [Sched Web Scraper] ──> [PDF Magic-Byte Validator] ──> [Karpathy Synthesizer]
│                                                                │
│                                                     [GroundingCritic Gate]
│                                                                ▼
└──────────────────────────────────────────────────────────────┬──────────────┘
                                                               │
                                                               ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                    HIGH-PERFORMANCE DATA & INDEX LAYER                      │
│        wiki/index.json  │  agntcon2026.sqlite (FTS5 BM25 + BGE Embeddings)   │
│        wiki/sources/*.md  │  wiki/concepts/*.md  │  crm.sqlite (Inquiries)  │
└──────────────────────────────────────┬──────────────────────────────────────┘
                                       │
                                       ▼
┌─────────────────────────────────────────────────────────────────────────────┐
│                 UNIFIED GATEWAY & MCP SERVER (serve.py)                     │
│  ┌───────────────────────────────┐     ┌──────────────────────────────────┐ │
│  │   Active Concurrency Queue    │     │      8 Native MCP Tools          │ │
│  │   (asyncio.Semaphore(2))      │     │  search_talks   │ get_page       │ │
│  │   45s lease timeout           │     │  answer_conf    │ get_talk       │ │
│  │   Telemetry /api/queue-status │     │  list_talks     │ list_concepts  │ │
│  └──────────────┬────────────────┘     │  get_conf_prof  │ get_queue_stat │ │
│                 │                      └─────────────────┬────────────────┘ │
└─────────────────┼────────────────────────────────────────┼──────────────────┘
                  ▼                                        ▼
┌──────────────────────────────────┐     ┌──────────────────────────────────┐
│        WEB CLIENT (app.js)       │     │       AGENT / IDE CLIENTS        │
│  • #chat-queue-indicator live UX │     │  • Cursor (.cursor/mcp.json)     │
│  • Obsidian 2nd-Brain JSZip      │     │  • Claude Desktop (mcp.json)     │
│  • Client-Side BYOM (Ollama/LM)  │     │  • OpenClaw Gateway Agent        │
└──────────────────────────────────┘     └──────────────────────────────────┘

Related MCP server: indico-mcp

🚀 Quick Start (Local Setup in 30 Seconds)

Option A: Using uv (Recommended)

git clone https://github.com/asoshnin/AGNT_MCP_con_2026.git
cd AGNT_MCP_con_2026
uv venv
uv pip install -r requirements.txt
uv run python serve.py

Option B: Using standard Python venv

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python serve.py --port 8080

Open http://localhost:8080 in your browser.

🤖 Model Context Protocol (MCP) Server Setup

This repository provides a standard JSON-RPC 2.0 stdio MCP server exposing 8 deterministic tools for agentic systems:

1. Claude Desktop Setup

Add the following snippet to your claude_desktop_config.json:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

• Windows: %APPDATA%\Claude\claude_desktop_config.json

• Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "agntcon-2026": {
      "command": "/absolute/path/to/.venv/bin/python",
      "args": ["/absolute/path/to/02_public_hub/mcp_server.py"]
    }
  }
}

2. Cursor IDE Setup

Add to .cursor/mcp.json in your workspace root:

{
  "mcpServers": {
    "agntcon-2026": {
      "command": "python",
      "args": ["mcp_server.py"]
    }
  }
}

3. OpenClaw Setup

Add to your openclaw.json under plugins.mcp.servers:

{
  "agntcon": {
    "command": "python",
    "args": ["/absolute/path/to/02_public_hub/mcp_server.py"]
  }
}

🛡️ Active Concurrency Queue & Zero-Cost Failover (Sprint P-04e)

The cloud gateway shields upstream community inference endpoints through an active FIFO queue:

• Bounded Concurrency: MAX_CONCURRENT_CHATS = 2 simultaneous tasks with asyncio.Semaphore(2).

• 45-Second Lease Timeout: Requests exceeding 45 seconds return HTTP 504 Gateway Timeout.

• Capacity Gate: If waiting queue exceeds 10 requests, immediate HTTP 429 with Retry-After: 15.

• Live UI Telemetry: Client polls /api/queue-status every 2s, rendering live queue positioning (Position in queue: #1 (estimated wait: ~3s)...).

• Free Gateway Cascade: Primary openrouter/free ➔ Secondary Kilocode / NVIDIA NIM free tiers ➔ Honest HTTP 503 capacity notice if all are saturated.

• Strict Zero Financial Harm: Never falls back to billable personal API keys.

🛠️ Bring Your Own Model (BYOM) / Local Offline Setup

Visitors who prefer 100% offline, zero-telemetry local inference can configure local models directly in the web UI Settings drawer:

Local LM Studio

1. Launch LM Studio and load any instruction-tuned model.

2. Start the Local Inference Server on port 1234 with CORS enabled.

3. In Hub Settings, select Local Ollama / LM Studio (http://127.0.0.1:1234/v1).

Local Ollama

1. Start Ollama with browser origin access:

   OLLAMA_ORIGINS="*" ollama serve

2. Pull a recommended model:

   • Standard Laptop (8–16 GB RAM): ollama pull qwen2.5:7b-instruct or ollama pull llama3.1:8b

   • Lightweight (4–8 GB RAM): ollama pull llama3.2:3b-instruct

   • High-End / GPU (16+ GB VRAM): ollama pull qwen2.5:14b-instruct or ollama pull deepseek-r1:14b

3. In Hub Settings, set Base URL to http://127.0.0.1:11434/v1.

Note: All BYOM queries run strictly browser-to-model via window.fetch(). Custom keys and prompts are never sent to or logged by the server.

🧠 Obsidian Second-Brain Export

Download pre-formatted markdown notes tailored for Obsidian:

1. Click 📦 My Track in the header.

2. Select desired talks or click Select All (113 Talks).

3. Click Download Obsidian Vault (.zip).

4. Client-side JSZip packages the notes with complete YAML frontmatter, tags, and bi-directional [[wikilinks]] with zero server computation.

🧪 Testing & Quality Gates

Run the automated test suite and linter:

# Run 21 deterministic unit & integration tests
pytest tests/ -v

# Run Ruff linter
ruff check .

# Run AST secrets & credential leak audit
python ../scripts/audit_secrets.py

# Run Concurrency burst harness (4 concurrent requests)
python ../scripts/test_queue_concurrency.py

⚖️ Open-Source Governance & License

• Source Code: Released under the permissive MIT License.

• Contributing: Please review CONTRIBUTING.md before submitting pull requests.

• Security & Responsible Disclosure: See SECURITY.md.

• Conference Attribution: Organized by The Linux Foundation and the Agentic AI Foundation.

• Engineered by: vwoosh.com.