Lm Server

The LiquidMind MCP server. It exposes a small set of READ and PROPOSE tools to Claude (Cowork) and translates each tool call into an HTTP call against the existing Liquid-Mind-CRM API. It holds no business logic and no platform credentials — it is a thin, authenticated router that sits in front of tools that already exist.

Claude Cowork ──HTTPS (bearer)──▶ lm-mcp ──HTTP (service token)──▶ Liquid-Mind-CRM API
   (connector)                    (this)                            (truth ledger + AI_Action_Queue)

Scope (v1)

• READ tools — pull from the truth ledger via the CRM API. No side effects.
  • list_clients, resolve_client, get_series_performance, get_campaign_performance, list_tasks, run_ad_review
• PROPOSE tools — write a row to the CRM AI_Action_Queue (status = 'proposed'). They never touch a campaign.
  • propose_bid_change, propose_budget_shift, propose_audience_rebuild, propose_campaign_state, propose_new_campaign

There is no EXECUTE in v1. Nothing this server does moves money — the worst a compromised token can do is read data and insert proposal rows a human must still approve. EXECUTE (apply_approved_action) is deliberately deferred (see Roadmap).

Layout

src/
  index.ts              Express + streamable-HTTP transport + incoming bearer auth
  server.ts             Builds the McpServer and registers tools
  config.ts             Env loading/validation (all endpoints are config vars)
  auth.ts               Bearer check for the /mcp endpoints
  clients/
    crmClient.ts        Thin HTTP client for the CRM API
    webToolsClient.ts   Thin client for the WebPages PHP tools (reserved for EXECUTE)
  tools/
    read.ts             READ tools
    propose.ts          PROPOSE tools
    index.ts, util.ts
Dockerfile, docker-compose.yml, cloudflared/config.example.yml, .env.example

Run locally

cp .env.example .env      # set MCP_AUTH_TOKEN and CRM_BASE_URL / CRM_SERVICE_TOKEN
npm install
npm run build
npm start                 # listens on :8930
curl localhost:8930/health

Run with Docker

cp .env.example .env      # fill in values
docker compose up -d --build

Bind stays on 127.0.0.1:8930; put Cloudflare in front for the public hostname.

Cloudflare

Give it its own subdomain — lm-mcp.website.com — not a path under the website.

• Preferred: a Cloudflare Tunnel so the droplet needs no open inbound port. Either run the token-based sidecar (uncomment cloudflared in docker-compose.yml, set TUNNEL_TOKEN) or a locally-installed cloudflared with cloudflared/config.example.yml.
• Do not put interactive Cloudflare Access / SSO in front of /mcp — the client is Anthropic-hosted, not a browser; an interactive login breaks the connector. Use the app's bearer token for identity and Cloudflare for the non-interactive layers (WAF, rate limiting, optional IP allowlist).
• Disable caching for lm-mcp.* and confirm streaming works end-to-end through the proxy.

The connector URL Claude uses is https://lm-mcp.website.com/mcp, with Authorization: Bearer <MCP_AUTH_TOKEN>.

Configuration

Var	Required	Purpose
MCP_AUTH_TOKEN	yes	Bearer token the MCP client must present. openssl rand -hex 32.
CRM_BASE_URL	yes	Base URL of the CRM API (localhost when co-located; private VPC IP when split out).
CRM_SERVICE_TOKEN	yes*	Service credential sent to the CRM as Authorization: Bearer.
WEBTOOLS_BASE_URL	no	Reserved for EXECUTE.
WEBTOOLS_SERVICE_TOKEN	no	Reserved for EXECUTE.
PORT	no	Default 8930.

* Required in practice because the CRM API is behind requireAuth. See below.

CRM-side contract (what this server depends on)

This repo assumes two things on the CRM. They are not in this repo — they live in Liquid-Mind-CRM:

1. A service credential. The CRM routes are gated by requireAuth(...). The MCP server sends CRM_SERVICE_TOKEN as a bearer token; the CRM must accept a non-interactive service identity (a long-lived service JWT, or a dedicated middleware branch) with a role that permits the READ routes and the actions route.

2. The AI_Action_Queue endpoint + table. The PROPOSE tools POST /api/actions with this envelope:

   {
     "actionType": "bid_change",
     "clientId": 42,
     "seriesASIN": "B0...",
     "platform": "AMG",
     "rationale": "…",
     "expectedImpactUsd": 430,
     "modelBasis": "…",
     "currentValue": { "…": "…" },
     "proposedValue": { "…": "…" },
     "proposedBy": "claude"
   }
   

   The endpoint should insert a row in status = 'proposed' and return the created row (including its actionId). The approve/deny UI and the atomic approved-only execution gate live on the CRM side. (Ask Claude for the AI_Action_Queue migration + routes — designed but not yet added.)

Until #2 exists, the READ tools work fully and the PROPOSE tools will return a clear error from the CRM (404) — which is the expected state before the queue is built.

Adding a tool

1. Add a server.registerTool(name, { title, description, inputSchema }, handler) in tools/read.ts or tools/propose.ts.
2. The handler should be thin: call crm.get/post/... and return ok(data) / fail(msg). No business logic here — extend the CRM instead.
3. Keep the tool list small and parameterized; prefer one flexible tool over many near-duplicates.

Roadmap

• EXECUTE phase: add apply_approved_action(actionId) — looks up an APPROVED AI_Action_Queue row, routes to the matching WebPages tool via webToolsClient (or enqueues the existing Redis worker job), writes back the result. Refuses anything not in approved state.
• Surface C: wrap the AMS_Automation scripts (e.g. pnl_sheet_filler.py) behind a job-runner or the existing Redis queue so they become EXECUTE targets.
• Per-manager identity: swap the shared bearer token for OAuth if you want per-account-manager attribution on proposals.