> For the complete documentation index, see [llms.txt](https://docs.owkin.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.owkin.com/connect-and-integrate/api-documentation.md). # API documentation **Architecture pattern.** K Pro exposes two programmable surfaces: a REST API for synchronous job submission, status polling, and result retrieval (structured JSON with provenance), and an MCP server endpoint that lets external agents discover and invoke K Pro platform directly. Long-running work (e.g. literature mining, multi-omic queries, federated analyses) runs asynchronously through the orchestrator. Submissions return a job ID; completion may be delivered via webhook callback or polled status. **Representative use cases.** * **Batch target scoring.** A differential-expression pipeline submits a gene list; the orchestrator runs the target-evaluation agent over each entry and returns a ranked JSON payload with confidence and evidence links. * **Pipeline step.** Nextflow or Snakemake stages call K Pro as a typed REST node, blocking on a webhook callback before downstream steps proceed. * **Embedded reasoning.** A customer's own agent calls K Pro tools over MCP to compose target-evaluation reasoning into its own workflow without round-tripping data through a UI. --- # 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.owkin.com/connect-and-integrate/api-documentation.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.