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kelvinDtran

foundation-mcp-demo

by kelvinDtran

foundation-mcp-demo

An MCP server that lets an AI agent turn a messy CRM account list into a ranked list of accounts to work, with every step authorized, scoped, rate limited, and replayable from an audit log.

This is a demo I built, not production infrastructure. It runs on a laptop, serves no real traffic, and has not been load tested or security reviewed by anyone else.

What's real and what isn't

Data

Source

Tag in every result

Legal entities, LEIs, parent relationships

GLEIF API

real:gleif

Public company filings, 8-K item codes

SEC EDGAR

real:sec-edgar

Intent scores, buying stage, contacts

Generated locally

synthetic:6sense-shape

The synthetic fields use names copied from 6sense's public API reference (api.6sense.com/docs), such as company_buying_stage and contact_grade. The values are made up. All people in this repo are synthetic, including their names. Nothing here comes from 6sense or claims to describe how their platform works.

Why a stand-in: 6sense's MCP server (api.6sense.com/mcp) requires a customer login, so I couldn't build against it. See DECISIONS.md D1.

Related MCP server: enterprise-agent-lab

Status

  • Authorization server and MCP resource server, OAuth 2.1 end to end (scripts/oauth_smoke.py)

  • Data adapters with response snapshots

  • Hash-chained audit log and offline replay

  • The seven tools, matching, ranking

  • Proposals and approval

  • Rate limits and run budgets

  • Recorded agent runs

  • Docker Compose

Run it locally

Requires Python 3.12.

python -m venv .venv
.venv/Scripts/pip install -e ".[dev]"        # macOS/Linux: .venv/bin/pip
cp .env.example .env                          # then fill in the passwords and secret
python -m foundation_demo.auth                # authorization server on :9000
python -m foundation_demo.mcp_server          # MCP server on :8001
python scripts/oauth_smoke.py                 # walks the OAuth flow and checks the refusals

Design

Every non-obvious choice, the alternatives, and the strongest argument against each pick: DECISIONS.md.

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