NeuronSearchLab

Create a new ranking rule. Rule types: boost (increase score), bury (decrease score), pin (fix position), filter (remove items), cap (limit fraction), diversity (spread categories). Each rule has conditions (which items match) and an action (what to do).

create_rule

Create a new user segment with membership conditions. Condition types: behavioral (purchase/click history), demographic (age, location), computed (ML scores), item_interaction (affinity to categories or items). Example: power users who purchased 3+ times in 30 days.

create_segment

Start a model training job from an existing template. This consumes training quota and may incur usage charges; auto-promotion options can replace the model serving live traffic.

create_training_job

Deactivate a ranking pipeline so it stops serving results without being deleted.

deactivate_pipeline

Delete a recommendation context. This also removes associated pipelines and rules.

delete_context

Permanently delete an event type. This cannot be undone. Existing tracked events referencing this type will remain in the database but the type definition will be removed.

delete_event_type

Delete a ranking pipeline.

delete_pipeline

Delete a ranking rule.

delete_rule

Delete a user segment. Any rules referencing this segment will need to be updated.

delete_segment

Disable a ranking rule without deleting it. Useful for temporarily pausing rules during debugging or campaigns.

disable_rule

Enable a ranking rule that was previously disabled.

enable_rule

Explain why a specific item was (or would be) ranked at a particular position for a user. Returns a score breakdown showing embedding similarity and applied rules, plus a pipeline trace showing which stages passed or failed.

explain_ranking

Get the authenticated team's current billing plan, resolved resource limits, live resource counts, metered usage, and exact overages. Always call this first when the user asks to clean up plan-limit overages, reduce usage to plan limits, or check remaining capacity.

get_account_plan

Fetch one auto-generated recommendation section for a user through the hosted console bridge. Serving a section records attribution and metering data and preserves section, next_cursor, and done response metadata.

get_auto_recommendations

Get the full configuration of a specific recommendation context by ID.

get_context

Get the full configuration of a specific A/B experiment including variants, traffic splits, and current metrics.

get_experiment

Get the most recently calculated statistical results for a completed or running experiment without changing stored data.

get_experiment_results

Get served counts, event breakdown, watch/click counts, and click-through rate for a specific item over a time window.

get_item_analytics

Get the full configuration of a specific ranking pipeline by ID.

get_pipeline

Get ranking performance metrics for a pipeline or context: CTR by position, overall CTR, conversion rate, diversity score, catalogue coverage, and revenue per session.

get_ranking_metrics

Fetch personalised recommendations for a user through the hosted console bridge. Serving a result records attribution and metering data and returns a request_id for subsequent events.

get_recommendations

Get the full configuration of a specific ranking rule by ID, including conditions and action.

get_rule

Get the full definition of a specific user segment, including all its conditions.

get_segment

Get a customer-facing status summary for a specific training job without returning infrastructure identifiers or raw logs.

get_training_job

Get served counts, event breakdown, unique-item activity, and click-through rate for a specific user over a time window.

get_user_analytics

List all API keys for the team with their names, scopes, environments, and last-used timestamps.

list_api_keys

List all recommendation contexts (feeds) configured for your team. Contexts define how recommendations are scoped, filtered, and grouped.

list_contexts

List all configured event types (click, view, purchase, etc.) for the team. Returns each event type with its ID, name, weight/value (0–100), and recorded event count.

list_event_types

List all A/B experiments with their current status (draft, running, paused, completed).

list_experiments

List configured third-party integrations (webhooks, data connectors, export targets, etc.).

list_integrations

List all ranking pipelines. Pipelines define the sequence of stages (candidate generation, scoring, rules, post-processing) that transform raw candidates into a ranked feed.

list_pipelines

List ranking rules, optionally filtered by context. Rules modify the ranking output — boost, bury, pin, filter, cap, or diversify items.

list_rules

List all user segments. Segments group users by shared behaviours or attributes and can be referenced in rules and campaigns.

list_segments

List recent model training jobs with their status (running, completed, failed). Training jobs re-train the ranking model on new interaction data.

list_training_jobs

Use after try_demo or record_demo_interaction to fetch a fresh recommendation result for a reserved synthetic user. This is only for the NSL sample workspace and never reads or changes a real customer user.

get_demo_recommendations

Recalculate experiment metrics from current recommendation and event data, store the refreshed metrics, and return the updated experiment results.

refresh_experiment_results

Revoke an API key. This immediately invalidates the key and cannot be undone.

revoke_api_key

Search the recommendation catalogue by keyword. Returns matching items with their IDs, names, descriptions, and status. Useful for looking up specific items before updating or removing them.

search_items

Presentation-only tool. Call try_demo first, wait for stage ready, then pass its complete structured result here unchanged. Renders the synthetic user, recent activity, ranked recommendations, and safe interactive sample controls in ChatGPT. Do not call this for real customer data or a pending workspace.

render_demo_recommendations

Start an A/B experiment. Traffic will begin splitting between variants immediately. The experiment moves from draft to running status.

start_experiment