mltree-mcp
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TDQS
Scored across 21 tools
Most tools target a distinct resource+action: steps (add_step, connect_steps, scan_project), runs (log_run, run_step, list_runs, best_run, run_log, cancel_run), sweeps (start_sweep, finish_sweep, run_sweep), and outputs (get_pipeline, open_map, export_map). The closest overlaps are start_sweep vs run_sweep (explicitly cross-referenced in the descriptions) and add_step vs scan_project (manual upsert vs static analysis), both of which are clarified in text. gnn_summary is a narrowly scoped inspection tool that stands clearly apart.
Nearly all names are snake_case verb_noun (add_step, connect_steps, list_runs, log_run, compare_runs, run_step, cancel_run, export_map). A few deviate from that pattern—best_run (adjective_noun), data_status (noun_noun) and gnn_summary (noun_verb)—but they remain readable and unambiguous. No mixing of camelCase or inconsistent delimiters.
At 21 tools the surface sits in the heavy band, spread across pipeline modeling, run logging/execution, sweeps, data versioning, notes/tags, and three output mechanisms (viewer, pipeline read, HTML export). Each tool maps to a recognizable capability, so nothing is obviously redundant, but the set is larger than the core job strictly requires.
Lifecycle coverage is broad (init_project, scan_project, add/connect steps, run/log/compare/tag/note, sweeps, export), but there is no way to delete a step, run, or note, and no tool to remove or edit an edge once created with connect_steps. Those omissions are notable in a mutable graph store, since mistakes require editing underlying files by hand. Agents can work around gaps but cannot fully clean up the model.