LLX Agent — MCP Skills Library

A confidential "skills library": employees use Codex locally, which auto-calls these skills over MCP. The skill code/data run in the cloud and are never downloaded locally, so employees can use the skills but cannot read them.

Full architecture & the 7-phase rollout plan: see ../workflow/mcp structure.md. This README is the operational guide (how to run, change, redeploy); that doc explains the why.

Status: ✅ Live on Azure (since 2026-06-29)

Related MCP server: MCP Practice

How employees connect (Codex)

Each employee needs Codex (≥ 0.144, which can connect to a remote MCP server directly). Two files go in their .codex folder:

• Windows: C:\Users\<username>\.codex\

• Mac/Linux: ~/.codex/

File 1 — config.toml (connects to the cloud)

Add this block to their Codex config file — it holds only the URL, no skill content:

[mcp_servers.llx-skills]
url = "https://llx-mcp.delightfuldesert-f5bbaa56.eastus.azurecontainerapps.io/mcp"
startup_timeout_sec = 60
tool_timeout_sec = 120

(Copy-paste ready template: codex-config.toml.)

Why url and not a command/npx mcp-remote bridge: Codex ≥ 0.144 speaks streamable HTTP directly. The npx bridge cold-started slowly and intermittently dropped the handshake; the direct url is more reliable. The timeouts give the cloud container time to wake from idle.

File 2 — AGENTS.md (forces Codex to always use the cloud)

Copy AGENTS.md to ~/.codex/AGENTS.md. This file is required.

Without it, a generic request like "list the database collections" can make Codex query a local database it happens to find nearby, instead of the cloud skills. AGENTS.md is a standing company rule that tells Codex: for any company data/skill request, always use the llx-skills cloud tools — never local files. (Verified: with the rule in place, even a vague prompt from a folder full of local DB configs correctly routes to the cloud.)

Then

Save both files, restart Codex, and just ask naturally — Codex auto-calls the matching skill in the cloud.

Skills

Each skill is its own file in server/skills/. server/main.py auto-loads every file in that folder at startup, so adding a skill = drop a new .py file in server/skills/ (copy an existing one as a template) — nothing else to edit.

Project layout

server/
  main.py        # entry point — auto-loads every skill (rarely touch)
  app.py         # the shared MCP server instance
  skills/        # ONE FILE PER SKILL  ← add / edit skills here
requirements.txt   # Python dependencies
Dockerfile         # how Azure packages the server
codex-config.toml  # employee client config (points at the cloud endpoint)
AGENTS.md          # employee Codex rule — always use the cloud, never local

Change a skill & redeploy

Editing GitHub does NOT auto-update Azure. The full loop:

1. Add or edit a file in server/skills/, commit, and push to GitHub.

2. Open Azure Cloud Shell: go to https://portal.azure.com, click the >_ icon in the top bar, choose Bash.

3. Run these two commands (no local Docker / CLI needed):

az acr build --registry cafa6fd6c51facr --image llx-mcp:v2 https://github.com/Cathylixi/LLX-Agent-MCP-Implementation.git
az containerapp update --name llx-mcp --resource-group LLXSolutions --image cafa6fd6c51facr.azurecr.io/llx-mcp:v2

Why update (not up): update only swaps the image and keeps the existing ingress and secrets/env vars (like the database MONGO_URI). Use it for all redeploys after the first one.

Why manual: auto-deploy needs a "service principal", which the org account ai@llxsolutions.com isn't allowed to create — so we build & deploy by hand.

Tag note: we always reuse the same tag (currently :v2), so each deploy overwrites the last (no version history). Bump to :v3, :v4, … in both commands if you want rollback points.

Connecting a database (Azure Cosmos DB)

The server can query the company database server-side and return only the results, so employees never see the database address or password. Connected since 2026-06-29.

• Database: Azure Cosmos DB (MongoDB API), database llxdocument, cluster llx-solutions-msft5.

• Driver: pymongo[srv] in requirements.txt (the +srv URI needs dnspython).

• Skill: the database skill is a file in server/skills/. It reads the connection string from the MONGO_URI env var and queries the DB server-side.

• Full write-up: ../workflow/connecting database.md.

Golden rules: (1) the connection string is a secret — it lives in an encrypted Azure secret, never in the code/GitHub; (2) expose specific, read-only query skills, never a generic "run any SQL" skill.

How it was deployed (run in Azure Cloud Shell)

# 1. build the image (includes pymongo[srv])
az acr build --registry cafa6fd6c51facr --image llx-mcp:v2 https://github.com/Cathylixi/LLX-Agent-MCP-Implementation.git

# 2. store the connection string as an encrypted secret
#    (copy the value from AI-for-Word/backend/.env line 8; keep the single quotes)
az containerapp secret set --name llx-mcp --resource-group LLXSolutions --secrets mongo-uri='<CONNECTION_STRING>'

# 3. deploy the image AND wire the secret to the MONGO_URI env var
az containerapp update --name llx-mcp --resource-group LLXSolutions --image cafa6fd6c51facr.azurecr.io/llx-mcp:v2 --set-env-vars MONGO_URI=secretref:mongo-uri

To change the connection string later, re-run step 2 only (then restart a revision). To add new DB query skills, edit main.py and redeploy (steps 1 + 3).

If the connection times out: open the Cosmos DB in the portal → Networking → allow access from Azure services / public Azure datacenters.

Verify it's working

After deploying (or any time), check the live server with a quick MCP client:

pip install mcp        # once
python - <<'PY'
import asyncio
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
URL = "https://llx-mcp.delightfuldesert-f5bbaa56.eastus.azurecontainerapps.io/mcp"
async def main():
    async with streamablehttp_client(URL) as (r, w, _):
        async with ClientSession(r, w) as s:
            await s.initialize()
            print([t.name for t in (await s.list_tools()).tools])
            print((await s.call_tool("db_list_collections", {})).content[0].text)
asyncio.run(main())
PY

Expect it to print the available tool names and the database collections. (Or in Codex with the config.toml above, just ask it to list the collections.)

⚠️ Security gap (fix before real data)

The endpoint has no authentication — anyone with the URL can call it.

• ✅ Outsiders cannot read the skill code/prompts (those stay server-side).

• ⚠️ But they can call the skills, get the results, see tool names, and burn cost.

Fine for the fake-data demo. Once skills return real confidential data, add token auth so only employees can call them.

Local development (optional)

To test changes on your own machine before deploying:

pip install -r requirements.txt
python server/main.py          # serves at http://127.0.0.1:8000/mcp

Temporarily point your Codex config.toml at http://127.0.0.1:8000/mcp (same block, just swap the URL), open Codex, and try a skill. Restart the server after each code change (a stale server keeps the old port 8000 and your new skill won't show up).