P2Predict MCP server

<!-- mcp-name: io.github.ahmed-khalil-hafsi/p2predict -->

The right price is already in your data.

P2Predict turns your purchasing history into a price model your team can talk to. Ask what a part should cost, why, and how sure the answer is, in plain language, through the AI agent you already use.

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Set up in one command:

pip install "p2predict[mcp]"

Built for procurement and engineering teams in automotive, semiconductors, electronics, industrials, pharma, and chemicals.

The problem

Most of what a part costs is decided upstream, in a design review procurement never sees. An engineer tightens a tolerance beyond what the application needs, or locks the part to a single supplier, and the cost rides downstream with no number attached. By the time the BOM reaches procurement, the expensive decisions are frozen and all that is left to negotiate is the rounding.

Then the quote arrives. The supplier knows their cost to the cent; you have last year's PO and a few days to respond. Multiply that across every line you buy and the leaks look the same everywhere: premiums no one benchmarked, specs no one costed, a number you cannot defend when finance asks where the savings went.

The answers are already in your purchase history. Nobody has had the time to dig them out.

What P2Predict does

P2Predict learns from your own purchasing data what drives the price of a part: supplier, material, size, spec, region. Then it gives your team a defensible target for any new or proposed part. Your category managers ask in plain English. The answer comes back grounded in what you have actually paid.

It runs on your machine, on your data, through any AI agent your team already uses. Nothing is uploaded. No vendor catalog, no cloud, no per-seat data-sharing.

What it is, and what it isn't

P2Predict is parametric price prediction. It learns the fundamental pricing structure in your historical buying data and benchmarks any part against it, the ones you are about to buy and the ones you already do. Being precise about that is the whole point, so here is the honest scope.

What it does

• Learns from the prices you have actually paid and predicts what a similar part should cost.
• Attributes that predicted price to each spec and to the supplier, so you can see what is moving the number.
• Puts a calibrated likely-range on every estimate and flags where the data is too thin to trust.
• Improves as you give it more of your own purchase history.

What it does not do

Shortened. The full README is on GitHub.