An MCP server on top of ICO: an agent writes typed operators, composes them into pipelines, runs and profiles them, and turns what works into reviewed code.
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# ico-mcp [](https://github.com/apriori3d/ico-mcp/actions/workflows/ci.yml)  [](https://opensource.org/licenses/Apache-2.0) An MCP server on top of [ICO](https://github.com/apriori3d/ico) with which an agent builds data pipelines from its own operators. The agent writes operators, small typed Python functions, and composes them into pipelines as JSON specs; the server checks, runs and profiles them. Operators that proved themselves in runs become the domain's stable code through a pull request a human reviews, and the next task starts from them instead of a blank script. - **Work accumulates.** Each task adds operators and pipelines to the domain's library instead of one-off scripts. - **Errors before the run.** Types are checked at every link before anything runs, so a broken pipeline does not burn GPU time. - **Fewer tokens.** The agent finds operators as compact cards by input and output type instead of reading code, runs saved pipelines by name, and gets data back as paths; long runs go on in the background. - **Visible runs.** The Live Board shows the plan and the progress of every node, the profiler where time and memory go, `sample_run` what flows out of a node; a run can be cancelled between items. - **Local or GPU.** The same pipeline runs here or on a RunPod GPU: the job carries the operators' code, model weights are pinned assets. ``` search_operators → validate → run → save_pipeline → crystallize → PR a step is missing → propose_operator, propose_types ``` ## How it is built ```mermaid flowchart TB A["agent"] -- "MCP tools" --> S["ico-mcp server, Live Board"] S --> kernels subgraph kernels["kernels"] direction LR K["main: catalog, validate, plans"] ~~~ J["one p…
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