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Scored across 4 tools
Each tool occupies a distinct pipeline stage: slicing context, finding seed functions, running local LLM repair, and reporting hardware capacity. The only mild overlap is between neurocode_slice and neurocode_seeds, since both do static code analysis, but the descriptions frame them as complementary (seeds finds candidates, slice extracts bounded context). An agent can pick correctly in most cases.
All names share a consistent neurocode_ prefix with snake_case, which makes the family instantly recognizable. The suffix style varies slightly (verb 'slice', noun 'seeds', verb_noun 'repair_ollama', noun_noun 'hardware_health'), a minor deviation from a strict verb_noun pattern but still readable and predictable.
Four tools map cleanly onto the four stages of a local code-repair pipeline, so each earns its place. The set is on the lean side, but the narrow scope (single-turn Ollama repair) keeps it justified rather than thin.
The pipeline covers health check, candidate identification, context slicing, and repair, but stops at patch generation; there is no verification, test-run, or patch-application step, which is a notable gap for a repair workflow. There is also no way to select or configure models beyond the implicit local Ollama instance.