TrustPipe

An MCP server that grounds AI answers about a business in real, verified data — sourced live when needed, cached for reliability — instead of stale training data or hallucinated guesses.

Built for the MCP Hackathon. See docs/PLAN.md for the full plan.

Demo

Two runs of the same questions about Obsidian's real pricing, same model:

1. Without TrustPipe connected — baseline (often wrong/uncertain)

2. With TrustPipe connected — grounded, correct, cited answers

Accuracy scored against a written eval set (eval/eval_set.json), not vibes — see eval/run_eval.py.

Related MCP server: AgentRank MCP

How it works

get_company_facts(company_name, country_code, topic):

1. Checks a local cache first — instant, reliable, no network dependency.

2. On cache miss, runs live research with a hard timeout, caches the result for next time.

3. On network failure with no cache available, returns an honest "unavailable" status — never a guess.

Structure

trustpipe/
├── server/
│   ├── server.py          ← MCP server exposing get_company_facts
│   └── facts_cache/
│       └── obsidian.json  ← pre-verified data, sourced from obsidian.md/pricing
├── eval/
│   ├── eval_set.json      ← question/expected-answer pairs
│   └── run_eval.py        ← scores before/after accuracy
├── requirements.txt
└── .env.example

Setup

python -m venv .venv
source .venv/bin/activate   # or .venv\Scripts\activate on Windows
pip install -r requirements.txt
cp .env.example .env         # then fill in your Gemini API key

Status

🚧 Hackathon build in progress.