mcp-support-tools

An MCP server (Model Context Protocol) that gives any MCP client, such as Claude Desktop, Cursor, or a LangGraph agent, the tools a customer-support agent needs: customer lookup, transaction history, knowledge-base search, and ticket escalation. Built on the official mcp Python SDK (2.x).

It packages the "business systems" side of the support bot I ran in production for two online gaming brands as a standalone, protocol-native server, on a fictional brand ("Astra Play") with fixture data. The agent side lives in support-agent-langgraph.

What it exposes

Kind	Name	Purpose
tool	`get_customer(customer_id)`	verification status, VIP tier, balance, active bonus
tool	`get_transactions(customer_id, limit)`	recent deposits and withdrawals, newest first
tool	`search_knowledge(query, category?, top_k)`	ranked knowledge-base sections with scores; falls back to unfiltered search when the category guess is wrong
tool	`create_ticket(customer_id, category, summary, transcript?)`	escalation with priority rules: VIP → urgent, withdrawals / responsible gaming → high
tool	`list_tickets()`	tickets created in the session
resource	`kb://docs`, `kb://docs/{doc_id}`	the knowledge base as browsable documents
prompt	`triage(customer_id?)`	a system prompt that tells the model when to call which tool

The server instructions field and the triage prompt encode the rules that mattered in production: look the customer up before talking about their money, state policies only from search results, escalate only on explicit request or when tools cannot resolve the issue.

Use it from Claude Desktop or Cursor

pip install git+https://github.com/stepbystepautomatization-jpg/mcp-support-tools

claude_desktop_config.json / .cursor/mcp.json:

{
  "mcpServers": {
    "support-tools": {
      "command": "mcp-support-tools"
    }
  }
}

Then ask: "Customer cust_2002 says their withdrawal was rejected. Why, and what should they do?" The model calls get_customer (unverified, active bonus), get_transactions (rejected, reason account_not_verified), search_knowledge("withdrawal rejected verification"), and answers from the data.

Streamable HTTP instead of stdio: mcp-support-tools --http (serves on :8000).

Use it from code

from mcp.client.client import Client
from mcp_support_tools.server import server

async with Client(server) as client:            # in-process, no subprocess
    tools = await client.list_tools()
    hits = await client.call_tool("search_knowledge", {"query": "minimum withdrawal"})

The same Client works with StdioServerParameters or an HTTP URL for a remote server.

Tests

pip install -e ".[dev]"
pytest          # 9 tests: tools, ranking, category fallback, ticket priorities, resources, prompt, path traversal

Tests drive a real MCP client connected in-process to the server, so the protocol layer (schemas, serialization, error mapping) is exercised, not just the Python functions.

Design notes

Structured errors, not exceptions. A missing customer returns {"error": "customer_not_found"} so the model can recover in the same turn instead of the tool call failing.
Search returns scores. The client can decide its own threshold; a score of 0 never comes back.
Fixtures are in-memory so the repo runs anywhere. Swapping them for the real back office is three functions.
Path safety on kb://docs/{doc_id} is enforced twice: by the SDK's resource security and by a parent-directory check in the handler.

License

MIT

support-tools