About ID Protocol
ID Protocol is a local CLI that turns reviewed personal preferences into compact, privacy-filtered context for reuse across AI tools and repositories. Markdown files remain the source of truth; the project does not provide an MCP server or synchronize vendor accounts.
Key features
- 01Maintain personal profiles and operating constraints
- 02Filter exported fields with privacy policies
- 03Generate compact portable context
- 04Validate, refresh, and compare profiles
- 05Keep local files as the source of truth
Use cases
- 01Reuse work preferences across AI tools
- 02Share only fields approved for export
- 03Review profile changes and generate context files
MCP support
The project does not provide an MCP server; it can export a privacy-filtered resource payload for an external MCP adapter.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project README
ID Protocol — portable context for AI tools
Keep your working preferences in files you control. ID turns a reviewed profile into compact, privacy-filtered context that can move between AI tools and repositories.
Try it in five minutes
Requires Python 3.11 or newer.
uv tool install id-protocol
mkdir my-id-profile && cd my-id-profile
idctl init --owner-id demo
Edit profiles/demo/profile.minimal.md, then build and inspect the portable output:
idctl validate --owner-id demo
idctl refresh-soul --owner-id demo
idctl export-compact --owner-id demo
idctl diff --owner-id demo --since 7d
The starter profile is provisional until you review it. Use an alias such as demo or my-work-profile; a legal name is unnecessary. ID does not upload generated files.
Files and ownership
| File | Role |
|---|---|
profile.minimal.md | Small owner-edited starting profile |
profile.core.md | Canonical profile when a fuller source is needed |
profile.extended.md | Optional detailed workflows and context |
privacy-policy.v1.json | Per-field export permissions |
handshake.md | Concise rules for applying context |
soul.md | Short derived bootstrap for an agent |
context.compact.json | Privacy-filtered portable context |
mcp.context.resource.json | Resource payload for an external MCP adapter |
Markdown profiles remain the source of truth. Generated JSON is a transport view and may omit nuance. The package is a local CLI; it does not provide an MCP server or synchronize vendor accounts.
Privacy behavior
Compact and MCP resource exports require a valid policy whose owner_id matches the requested profile. Unlisted fields stay local-only. Missing, malformed, mixed, conflicting, or owner-mismatched policies block export.
Legacy list policies from earlier releases are normalized in memory during validation and export. Their source files remain unchanged and their restrictions remain effective. --allow-unfiltered only permits a missing policy in a reviewed legacy workflow; it cannot bypass an invalid policy.
Review the resulting export before attaching it to another tool. See the privacy model, policy schema, and threat model.
ABVX toolchain
| Product | Responsibility | Tested release |
|---|---|---|
| ID | Human preferences and operating constraints | 0.5.2 |
| agentsgen | Repository instructions and commands | 0.5.0 |
| SET | Review-first workflow planning and exports | 0.4.0 |
| abvx-agent-skills | Optional reusable workflows | 0.15.0 |
These remain independently installable. ID does not install companions or grant tool permissions.
For a repository that already contains a reviewed ID profile:
idctl install-set-hook --path .
The adapter returns owner-local bootstrap pointers to SET and preserves an existing custom hook. See the SET integration guide.
Evidence
Three reproducible demos exercise installed onboarding, policy-filtered omission, legacy-policy compatibility, and the agentsgen/SET handoff with synthetic data. They measure local CLI behavior and runtime, not AI answer quality.
The historical benchmark snapshot is a small checked-in sample with explicit limits. AI-client behavior still depends on each client's configuration and context rules.
Develop
git clone https://github.com/markoblogo/ID.git
cd ID
python -m venv .venv
. .venv/bin/activate
python -m pip install ".[dev]" build twine
make validate
make drift-check
make coverage
make release-build
make release-check
Start with the documentation index, protocol, integration boundaries, and contributing guide. Use synthetic profiles in public issues and pull requests.
ABVX ecosystem
- AGENTS.md_generator — Exports reviewed context for repository guidance. Current release:
v0.5.1. - AGENTS.md_generator — Keeps repository guidance and machine-readable context current. Current release:
v0.5.1. - abvx-agent-skills — Uses shared, reviewable agent capabilities during maintenance. Current release:
v0.16.1.
This block is generated from the reviewed ABVX ecosystem registry.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.