Quotations MCP Server

Standalone MCP server exposing the quotations-app backend as tools. It's a thin HTTP client over the quotations REST API — no database of its own — so it stays decoupled and can point at local dev or the live Render backend.

MCP agent ──MCP──► server.py ──HTTP──► quotations-app backend ──► MongoDB

Tools

Purpose is conversational intelligence — let an agent answer natural-language questions about quotations (counts, values, who-has-what, lookups) and take actions.

Conversational / query (read):

READ-ONLY by design. This MCP only queries the customer's data and answers questions — it never writes (no create/reassign). Creating or modifying quotations is the customer app's job. The backend exposes only list + get, so filtering/aggregation is done here; prices/totals come only from the server-computed data — never invented.

Completion / status

A quotation is "completed" only if the customer's data stores that state. Today their backend hard-codes status:"draft" and never changes it, so quotation_stats / search_quotations(status=...) will only ever report draft. The moment their app writes a real status (e.g. completed/sent), these read tools surface it automatically — no change here.

Related MCP server: finance-mcp

Install

pip install -r requirements.txt   # mcp[cli], httpx

Run

# stdio (dev / desktop MCP clients)
python server.py

# HTTP endpoint (for the MCP agent to connect to)
MCP_TRANSPORT=http MCP_PORT=8200 python server.py
# → MCP endpoint at http://<host>:8200/mcp

# inspect/try tools
mcp dev server.py

Env

Notes

• Deployed separately from the quotations backend and from Oscar.

• The Oscar agent will later connect to this server (as an MCP client) via a new endpoint — this repo/folder does not depend on Oscar.