About hrevn-mcp-server
hrevn-mcp-server is a minimal stdio MCP server that connects AI agents to HREVN’s managed runtime for baseline diagnostics and workflow integrity checks. It keeps the logic in the managed API and returns structured results.
Key features
- 01Baseline checks
- 02Profile validation
- 03Bundle generation and verification
- 04Structured risk and remedy results
Use cases
- 01Check an AI workflow against a baseline
- 02Validate a profile and record
- 03Generate or verify an artifact bundle
MCP support
Exposes four MCP tools that call the HREVN managed API over stdio: baseline_check, profile_validate, generate_bundle, and verify_bundle.
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 READMEHREVN MCP Server
Minimal stdio MCP server for HREVN, backed by the live managed runtime at
https://api.hrevn.com.
Why this exists
This repo exposes HREVN as real MCP tools instead of ad hoc helper scripts. It stays intentionally thin:
- canonical semantics remain in the HREVN managed runtime
- the MCP layer only exposes stable tool contracts
- consequential results come back as structured output, including
risk_flags,remedy_payload, andcheck_id
Included tools
baseline_checkprofile_validategenerate_bundleverify_bundle
Environment
export HREVN_API_BASE_URL="https://api.hrevn.com"
export HREVN_API_KEY="replace-with-issued-alpha-key"
Optional environment variables:
HREVN_MCP_TIMEOUT_SECONDSHREVN_MCP_SERVER_NAMEHREVN_MCP_SERVER_VERSION
Install
Editable install
cd hrevn-mcp-server
python3 -m pip install -e .
From PyPI
python3 -m pip install hrevn-mcp-server
The console entry point is:
hrevn-mcp-server
Run
hrevn-mcp-server
The server uses MCP stdio transport.
Verify before using a client
hrevn-mcp-server --version
hrevn-mcp-server --list-tools
hrevn-mcp-server --self-test
If you are running directly from source without an installed entry point:
PYTHONPATH=src python3 -m hrevn_mcp_server.server --list-tools
PYTHONPATH=src python3 -m hrevn_mcp_server.server --self-test
The self-test runs a live baseline_check against the configured HREVN
managed API.
Tool contracts
baseline_check
Minimal payload:
{
"task_type": "ai_workflow",
"profile": "eu_readiness_profile",
"record": {
"agent_name": "example_agent",
"summary": "baseline smoke test"
},
"metadata": {
"surface": "mcp",
"stage": "pre_completion"
}
}
profile_validate
Minimal payload:
{
"profile": "eu_readiness_profile",
"record": {},
"metadata": {}
}
generate_bundle
Minimal payload:
{
"record": {},
"traces": [],
"options": {
"include_report_pdf": false
}
}
verify_bundle
Minimal payload:
{
"source": "/path/to/bundle-or-artifact"
}
Example MCP config
Use a local stdio configuration like this:
{
"mcpServers": {
"hrevn": {
"command": "python3",
"args": [
"-m",
"hrevn_mcp_server.server"
],
"env": {
"PYTHONPATH": "/ABSOLUTE/PATH/TO/hrevn-mcp-server/src",
"HREVN_API_BASE_URL": "https://api.hrevn.com",
"HREVN_API_KEY": "YOUR_HREVN_API_KEY"
}
}
}
}
See also:
- Antigravity setup guide
- Conservative experiment guide
- Antigravity MCP config example
- Antigravity baseline payload
Antigravity status
HREVN can already be explored experimentally in Google Antigravity through custom MCP configuration.
What is already validated:
- the HREVN MCP server runs locally
- the server exposes real tools backed by
https://api.hrevn.com baseline_checkreturns real structured results- Antigravity preview builds expose MCP configuration surfaces in the UI
What is not claimed yet:
- official native Antigravity integration
- guaranteed MCP tool injection into every Antigravity agent instance
- Antigravity-specific guardrail hooks beyond what has been directly observed
Registry metadata
This repo includes:
as a machine-readable manifest describing the MCP server, package, transport, and repository metadata.
Design rule
This server must not reimplement HREVN truth locally. It should expose stable MCP tools that call the managed API.
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.