Negbit MCP server

The Negbit Spec

An open quotation model for pre-processed knowledge bundles in machine-to-machine markets.

Version 1.0 · July 2026 · negbit.com · Nicolas Limare, ag3ntlab

This repository is the normative specification of the negbit pricing formula, its calibration defaults, the freshness rules, the domain half-life taxonomy, and the bundle audit protocol. The full derivation, the empirical calibration and the competitive analysis are in the white paper: read it online at negbit.com/paper or download the PDF.

Licensed CC BY-SA 4.0 (see License). The knowledge bundles you price with it remain entirely their owners' property.

1. What a negbit is

AI agents acquire task-relevant information two ways: by burning inference tokens to search, filter and synthesize raw sources, or by purchasing pre-processed, directly consumable context from another agent over machine-to-machine payment rails such as x402. A knowledge bundle is a curated set of text files built for agent consumption. A negbit is the unit this spec prices: one bundle of delivered negentropy, the ordering work that separates a curated bundle from the raw noise it was refined from.

The core claim of the model: the fair price of a bundle is the processing cost it saves its buyer, capped by what the answer is worth, decayed by the freshness of its domain, and split by bargaining weight.

2. The formula

The quote for a bundle of size $S$ tokens, age $a$ days, in a domain with half-life $t_{1/2}$ days:

$$ P^\ \;=\; \beta \cdot \min\!\big[\,\Delta \mathrm{EVSI},\; C{\mathrm{avoided}}\,\big] \cdot 2^{-a/t_{1/2}} $$

with a floor at the seller's amortized production cost per expected sale.

Terms:

3. The avoided cost

$$ C{\mathrm{avoided}} \;=\; (1+r{\mathrm{fail}})\big[\, c{\mathrm{in}}\,\rho\,S \;+\; c{\mathrm{out}}\,\sigma\,S \;+\; c{\mathrm{tool}}\,n \,\big] \;+\; w\,\Delta\tau \;-\; c{\mathrm{in}}\,S \;-\; C_{\mathrm{verif}} $$

Reading it left to right: what the buyer would have spent reading the raw sources, synthesizing them, and paying for tool calls, inflated by the failure rate of autonomous research runs, plus the value of the time saved; minus the cost of reading the bundle itself and the cost of verifying the seller's claims.

The refinement ratio $\rho$ is the load-bearing quantity: how many raw tokens an agent would need to ingest per curated token delivered. Measured on real deep-research agent runs, honest values run 5 to 25 (raw dump ≈ 3, summarized ≈ 7, curated ≈ 15, deep-researched ≈ 25, expert distillation ≈ 35). $\rho$ is also where diffusion bites: knowledge that is ubiquitous and already well organized elsewhere has a collapsed effective $\rho$ regardless of how beautifully it is arranged. The formula prices the rarity of the refinement work, not the beauty of the order.

Calibration defaults (July 2026, from the paper §5)

Shortened. The full README is on GitHub.