context-firewall

A local MCP server that answers agents' questions about you — through a policy.

Share your allergies with the restaurant-booking agent. Refuse your salary to everyone by default. Every request logged, every grant revocable, nothing in the cloud.

npx context-firewall init
npx context-firewall enroll "claude-code"
# paste the printed token into claude-code's MCP config env — see docs/clients/claude-code.md
npx context-firewall serve

See it work without any client at all:

git clone https://github.com/ketriumlabs/context-firewall.git && cd context-firewall
pnpm install && pnpm build
node examples/demo/restaurant-agent.mjs

That script is the whole pitch in ~30 lines of console output: an agent asks for allergies, gets refused until a human approves a single-use grant, reads it once, gets refused again on the second attempt (grant consumed), then asks for salary and is refused throughout — no scenario in the demo ever grants that. Self-verifying — it exits non-zero if the behavior ever drifts from this description.

Why

Portable AI-context projects (open-context, personal-context-manager, Stanford Digital Economy Lab's Human Context Protocol work) are converging on "give agents a file about you" — but none of them do field-level privacy. This does: a deny-by-default policy engine between your context and every agent that asks about it, so "share diet preferences" and "share salary" are two different, separately-revocable decisions, not one all-or-nothing file handoff.

The two files

• context.yaml — your data, namespaced (identity, preferences, health, finance, ...). Human-editable, diffable, safe to back up in a private git repo.
• policy.yaml — who gets what. Per-client, per-field grants with expiry (once / a TTL date / never). cat it any time to see exactly who can see what.

Neither file is a database. Both are plain YAML you can read yourself — full field reference in docs/format.md, with editor autocomplete via the published JSON Schemas in schemas/.

Threat model — read this before trusting it

See docs/threat-model.md. Short version: this is a consent and organization tool, not a sandbox. It does not protect against local malware with filesystem access, a client that lies about its own identity (a bearer token, not a cryptographic proof, is what establishes identity in v0.1), or — yet — data at rest without your OS's own disk encryption. It does give you a real audit trail, revocable per-field grants, and policy files you can read yourself instead of trusting a vendor's opaque memory feature.

Importing existing memory

context-firewall import chatgpt path/to/export.json
context-firewall import claude path/to/memory-export.json

Both write a *-imported-draft.yaml file — extracted text snippets under an imported namespace with instructions to sort them into real namespaces by hand. Deliberately not "smart": guessing that a memory string is health vs. finance and getting it wrong silently would be worse than not guessing.

CLI reference

Command	What
init	Create context.yaml + policy.yaml
enroll <name>	Register an MCP client, print its one-time token
serve	Start the MCP server (reads CONTEXT_FIREWALL_TOKEN from env)
status	Show pending requests and current grants
approve <id> [--once | --ttl-days N]	Approve a pending request
deny <id>	Deny a pending request (remembered — never re-asked)
import <chatgpt|claude> <file>	Draft context.yaml from an export

All commands accept --dir <path> to use a data directory other than ~/.context-firewall.

Citations

Namespace layout and field taxonomy adapted from the Stanford Digital Economy Lab's Human Context Protocol work and the open-context project's field categories — see docs/format.md for the mapping. This project doesn't invent a new standard; if one of these converges into something real, context-firewall converges with it.

Roadmap

See docs/roadmap.md — OS-keychain encryption at rest is the tracked v0.2 flagship.

Contributing

See CONTRIBUTING.md. Policy/consent changes get extra scrutiny — see SECURITY.md.

License

Apache-2.0