ToolBridge

A governed MCP integration connecting an AI agent to a mock CRM, support ticket store, and knowledge base, with role-based access control and field redaction enforced outside the model. Built to demonstrate production-safe agent wiring: the model cannot widen its own access, sensitive fields are stripped before the agent ever sees them, and every reply draft goes through a human-review checkpoint before it can be sent.

What it demonstrates

• Governed MCP server built with the official Python mcp SDK (FastMCP), exposing five tools across three data sources.

• Role-based access control: the support_agent role cannot call the deal-pipeline tool at all; admin has full access. The role is fixed from the environment at startup, not negotiable by the model.

• Field redaction outside the model: billing fields (payment_method, billing_email, contract_value_usd) are stripped from customer records for support_agent before the data reaches the agent. The model sees a clean, role-scoped view.

• Grounded KB lookup: search_kb surfaces documented answers for the agent to cite, reducing hallucination.

• Human-in-the-loop draft step: draft_reply returns a review-pending object and explicitly does not send anything automatically.

Related MCP server: gov-mcp

Quick start

pip install -r requirements.txt
python3 demo.py

No API key required. The demo simulates a full support workflow and prints a narrated transcript showing governance at each step.

To see the admin path (unredacted data, deal pipeline access):

TOOLBRIDGE_ROLE=admin python3 demo.py

To run the governance test suite (12 tests, no pytest needed):

python3 -m tests.test_governance

Optional: if you have an Anthropic API key, you can run a real Claude tool-loop:

ANTHROPIC_API_KEY=sk-... USE_CLAUDE=1 python3 demo.py

File layout

toolbridge/
  policy.py       Role definitions and governance primitives (access check, redact)
  store.py        In-memory mock data: CRM, tickets, knowledge base
  server.py       MCP server -- five tools, all gated through policy
demo.py           Runnable demo: narrated transcript, zero API key needed
tests/
  test_governance.py  12 assertions covering redaction and access control
requirements.txt

Extending this

The governance layer (policy.py) and the data layer (store.py) are decoupled from the tool definitions. To connect real systems, replace the in-memory dicts in store.py with API clients (HubSpot, Zendesk, Confluence) while keeping policy.py and server.py unchanged. Add new roles by editing the ROLES dict in policy.py.