PETE MCP Gateway
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@PETE MCP Gatewayshow consolidated reports across all projects"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
PETE MCP Gateway
One remote MCP endpoint (https://mcp.by-pete.com/mcp, Streamable HTTP) that
gives every AI client — Claude, Claude Code, ChatGPT, Gemini CLI, Grok/xAI,
Pete Qwen — the same policy-checked business tools, shared cross-client
memory, durable jobs, and consolidated reporting across the copackers /
bypete / offduty projects plus Bunting and n0v8v operations.
Built to docs/SPEC.md. Status: deployment-sequence step 2 complete (isolated
gateway, auth, policy, memory, jobs, reports, connector adapters, tests);
steps 3–6 are host + credential work (docs/DEPLOY.md).
Layout
Path | What |
| MCP server (FastMCP, stateless Streamable HTTP) + bearer auth ASGI wrapper, |
| project × actor × action grants, daily budgets, cross-client effect keys, audit log |
| shared memory: fact/decision/suggestion, provenance, optimistic versioning |
| durable queue (SKIP LOCKED + leases, bounded retries), registered job types only, RULE_4 verification-or-unverified |
| adapter framework; declarative REST connectors in |
| operator CLI: create-client / grant / revoke / list |
| additive |
| SPEC, CONNECTORS walkthrough, CLIENTS hookup guide, DEPLOY |
Related MCP server: aziel-runtime
Tools
whoami, system_status, connections_status, connector_call,
memory_search, memory_record, memory_supersede, jobs_submit,
jobs_get, jobs_cancel, reports_get.
Adding a REST connector = one entry in gateway/registry.yaml (env var names
only — no secret value ever appears in this repo, per SECURITY_BOUNDARIES).
Quick start
python -m venv .venv && .venv/bin/pip install -r requirements.txt
cp .env.example .env # fill server-side only
.venv/bin/python -m pytest tests/ -q
.venv/bin/python -m gateway.servern0v8v LLC | GP3 methodology
This server cannot be deployed
Maintenance
Related MCP Connectors
Hosted MCP memory and agent control plane for durable conversations, jobs, and operations.
MCP server for your apps' tools and custom tools, plus hosted AI agents and approval-gated workflows
Project management shared by people and AI agents, with persistent project state through MCP.
One MCP endpoint for your company's tools, with per-person sign-in, policy and audit.
Related MCP Servers
- FlicenseNot gradedqualityAmaintenanceA production-ready MCP gateway and control plane that provides credential vault, policy engine, audit logging, and managed runtime for routing tool calls between AI agents and downstream MCP servers.59-
- AlicenseAqualityAmaintenanceAziel Runtime is a node-meshed MCP software orchestration suite for digital forensics, auditing, provenance, research, intelligence-support, and secure agent workflows, with a single gated execution path, compartmentalized engines, verifiable receipts, temporal integrity, and explicit capability refusal. It is not merely an API aggregator or generic tool router.362Apache 2.0
- AlicenseNot gradedqualityAmaintenanceEnables AI tools to uniformly discover, inspect, and call tools, prompts, and resources from multiple upstream MCP servers through a small set of fixed MCP tools, over stdio or HTTP.2MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI clients to connect to a single MCP endpoint that aggregates many MCP servers and built-in tools, with multi-user authentication, per-user server configuration, encrypted secrets, and a skills hub.MIT