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

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

MCP Servers