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Quotations MCP Server

by cbunny-2005
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

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):

Tool

Answers

quotation_stats()

"how many are pending?", "total pipeline value?", "who has the most?" — counts by status + value by assignee

search_quotations(company?, status?, assignee?, number?, min_total?, max_total?)

"show quotations assigned to Vijender over ₹10k" — filtered list + total value

find_by_number(quotation_number)

resolves a human number like QT-2026-000005 to the full quotation

list_quotations()

all quotations (compact)

get_quotation(quotation_id)

one full quotation by internal id

health()

backend reachability + count

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

Var

Default

Purpose

QUOTATIONS_API_URL

https://quotations-app.onrender.com

backend base URL

QUOTATIONS_TIMEOUT

30

HTTP timeout (bump for Render cold start ~50s)

MCP_TRANSPORT

stdio

stdio or http

MCP_HOST / MCP_PORT

127.0.0.1 / 8200

bind for http transport

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.

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityMaintained
ResponsivenessNo issues

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