> For the complete documentation index, see [llms.txt](https://docs.nfh.global/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.nfh.global/use-cases/agent-data-marketplace.md). # Data marketplace for agents MCP-native discovery and per-call metering for data products. An autonomous agent needs data to act — a pricing feed, a geospatial layer, a model output, an API, a slice of a training corpus. Today it gets that data through bilateral integrations and annual contracts negotiated by humans. That doesn't scale to thousands of agents making thousands of decisions an hour, each needing a different source for a few seconds. NFH fabric makes data a **tradable primitive** an agent can handle end to end in a single, signed network conversation: discover the right product, verify its quality and licence, pay per call, and consume it — no human in the loop, no standing contract. Discovery is exposed as MCP tools, so the agent treats a data market the way it treats any other tool. ## What changes * **Acquisition shifts from annual contracts to spot, per-call markets** — an agent buys exactly the data it needs, when it needs it. * **Lineage is signed** — consumers can prove what source they used, at what version, for which decision. * **New data providers reach autonomous customers** without an enterprise sales motion — publishing a product is the go-to-market. * **Agents compose sources at runtime** based on observed quality, freshness, and price, switching providers without re-integration. * **Licensing and purpose are enforced as credentials** — usage outside the agreed purpose is verifiable and refusable, not a contractual afterthought. ## Where to start Wrap one existing data API as an NFH fabric-published offering, add a credential for quality and freshness, and wire micropayment-per-call via [Tokenisation](https://docs.finternetlab.io/). Expose discovery as an MCP tool and have a test agent find, license, and call it end to end. The hardest design call is usually the licence-and-purpose credential model — solve that first. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.nfh.global/use-cases/agent-data-marketplace.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.