care-agent

A personal care-management agent, medical module first. Built as a reusable MCP + LangGraph skeleton — the medical domain is the first instance, not the only one. Finance, licensing, or other domains can reuse the same graph shape later with different tools and a different retrieval source plugged in, without touching the graph itself.

Deliberately a separate repo from J-Bishop-PA, not a merge into it. J-Bishop-PA sprawled across four phases and became hard to track. This repo is scoped to one domain on purpose. MCP's whole design point is that a client can connect to several servers at once — there's no need to share a codebase to use both alongside each other.

Why these choices

LangGraph over a linear pipeline. The domain has two real branches: does this query need literature research or just local history, and does the answer require an external action that needs sign-off first. A fixed chain can't express that; a state graph with conditional edges can. interrupt() plus a checkpointer gives human-in-the-loop for free instead of hand-rolling a pause/resume mechanism.

Chroma, embedded, local default embeddings. The corpus is small — tens to low hundreds of abstracts, not millions of documents — and cost matters. No hosted vector DB to stand up or pay for, no per-call embeddings API charge for a static, small corpus. Revisit this if the corpus grows past a few hundred documents or retrieval quality turns out to need more than a small local model can give.

PubMed plus a short allowlist, not general web search. RAG output is only as trustworthy as what's indexed. General web search pulls in forums and content-farm health content right alongside real research — the source restriction is a correctness decision, not a shortcut.

No send-capable tool anywhere in this server. Every provider-facing action is draft-only (draft_provider_email), mirroring J-Bishop-PA's draft_reply pattern. Enforced by tests/test_no_send_tool.py, not just a convention in a docstring — see the note below on why that distinction matters.

Synthesis never states a diagnostic or causal conclusion. The model has no clinical grounding to draw that line itself. Output is always framed as "here's what's published, here's what to ask your doctor," never a standalone conclusion — enforced in the system prompt for the synthesize node, checked again by the guardrail node.

Same model as judge, not a separate one. Cheaper and simpler at this scale — a second Claude call with a distinct system prompt. Known limitation, worth being upfront about: this is weaker separation than a genuinely independent judge model or human review. Fine here; wouldn't be at higher stakes.

On the guardrail-as-code test: an earlier project (Baysa Analytics) had a leakage-prevention test that looked real but mostly asserted on database rows rather than actual function output — it would pass even if the thing it was meant to catch happened. test_no_send_tool.py here is written against the actual registered tool list for that reason, not against something adjacent to it.

Status

Scaffolded, not built. Everything in graph/nodes.py, tools/*.py, and rag/*.py is a docstring and a NotImplementedError describing intent — see the build plan for what gets implemented in what order.

Setup

python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
cp .env.example .env  # fill in ANTHROPIC_API_KEY and PUBMED_EMAIL