MCP1

Remote MCP server delivering the Doggi (Careful Starter) investor-psychology curriculum as 7 tools. Built so Claude pulls only the lesson slice it needs per turn instead of re-ingesting the whole corpus.

Layout

| Path | What | |---|---| | server.py | Business logic. The DoggiMCP class — 7 tool handlers, reads from disk | | http_server.py | HTTP wrapper. Exposes DoggiMCP over MCP Streamable HTTP, with API-key auth | | coaching_session.py | Local client (Anthropic SDK). Useful for testing tool flow without deploying | | tests/demo.py | Smoke test, runs every tool | | data/ | Framework, archetype, interventions, progress (source of truth) | | lessons/ | Lesson 1–6 markdown (source of truth) | | Dockerfile, fly.toml, requirements.txt | Deploy config for Fly.io | | DEPLOY.md | Step-by-step deploy guide |

Run locally

# Smoke test, no API key needed
python3 tests/demo.py

# Run the HTTP server locally
MCP_API_KEY=test pip install -r requirements.txt
MCP_API_KEY=test python3 http_server.py
curl -H "X-API-Key: test" http://localhost:8000/mcp

# Live coaching client (needs ANTHROPIC_API_KEY + COACHING_MODEL)
python3 coaching_session.py

Deploy

See DEPLOY.md — Fly.io, ~20 minutes end to end.

Tools

1. get_lesson(n) — lesson 1–6
2. get_framework() — north star
3. get_archetype_triggers() — Doggi triggers, red flags, strengths
4. get_coaching_intervention(n) — coaching style, do/don't, example for lesson n
5. get_progress_state() — where the client is in the journey
6. analyze_response(text, n) — match user text against triggers and red flags
7. next_lesson(n) — sequencing

Customize

Everything lives in data/ and lessons/. Edit, commit, fly deploy. No code change needed.

• lessons/lesson{1-6}.md — lesson content
• data/archetype.json — triggers, red flags, strengths
• data/interventions.json — coaching style per lesson
• data/progress_state.json — client state (replace with DB query in production)

Design notes

• Don't put this README (or other docs) into the Claude Project space. Anything Claude reads on every turn defeats the MCP's purpose.
• Don't hard-code dated model IDs. coaching_session.py reads COACHING_MODEL from env.
• Source of truth is on disk, not in code. No embedded copies.
• Tool descriptions are intentionally short (≤6 words). The schemas describe inputs.
• API key auth is mandatory. http_server.py refuses to start without MCP_API_KEY.

Security

• API key required on all /mcp requests via X-API-Key header
• /health is open (Fly.io needs it for liveness checks)
• Path traversal blocked: lesson_num validated as int 1–6 before any file access
• Input size capped: analyze_response rejects payloads over 10KB
• Container runs as non-root user
• No secrets in source — MCP_API_KEY and ANTHROPIC_API_KEY come from env only
• .gitignore and .dockerignore exclude .env* and other secret patterns