Respan MCP Server
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TDQS
Scored across 67 tools
Most tools follow a distinct resource+action split with helpful cross-references (e.g., create_workflow explicitly defers to the typed creators), but several pairs have unclear boundaries: test_evaluator vs run_evaluator both 'verify an evaluator on one input,' retrieve_dataset_log vs get_log_detail vs list_dataset_logs all serve 'get log data,' and get_evaluation_pipeline overlaps with get_workflow since pipelines are workflows. An agent would frequently have to read very carefully to avoid picking the wrong tool.
The set overwhelmingly follows a consistent verb_noun snake_case pattern (list_*, get_*, create_*, update_*, delete_*, commit_*, deploy_*), making the surface predictable. Minor deviations stand out: retrieve_dataset_log breaks the get_* convention, filter_workflows is an odd list-variant, and get_spans_summary vs summarize_dataset_logs use different verbs for the same 'aggregated stats' concept.
67 tools is far beyond the usable range for an agent, even accounting for the genuinely broad domain (logs, traces, customers, prompts, datasets, experiments, evaluators, pipelines, workflows, organizations). The count is inflated by fine-grained operations and even a client-side workaround (get_experiment_score_averages) that exists to patch a backend bug, which signals the surface was exposed with minimal curation.
Lifecycle coverage is impressive: full CRUD plus versioning, committing, and deployment for prompts, evaluators, datasets, and workflows, with typed creators for automations, monitors, and exports. Notable gaps remain — no delete for evaluation pipelines, no prompt deletion or deactivation, no update/cancel for experiments — but these are workaround-able rather than blocking.