// SCAN REPORT

NeuronSearchLab

71 tools. 40 can modify or destroy data without limits.

9 destructive tools with no built-in limits. Policy required.

Last updated: 3 Jul 2026

40 can modify or destroy data

31 read-only

71 tools total

Community server · catalogue entry verified 03/07/2026

How to control NeuronSearchLab ↓

TOOL MAP

What NeuronSearchLab exposes to your agents

Read (31) Write / Execute (31) Destructive / Financial (9)

Running more than one server? Check your whole stack →

MOST DANGEROUS TOOLS

Critical Risk

The most dangerous NeuronSearchLab tools

Destructive · High cancel_training_job Cancelling a running training job is an irreversible action: the in-progress computation, accumulated gradients, and intermediate model state are lost and cannot be recovered. Destructive · High delete_campaign This tool permanently removes a campaign and its associated rules without the ability to undo the action. Destructive · High delete_context This tool performs irreversible deletion of data (contexts, pipelines, and rules). Destructive · High delete_event_type This tool irreversibly removes data (an event type) from the system with no recovery mechanism.

40 of NeuronSearchLab's 71 tools can modify, destroy, or commit something on every call — and an agent calls them with no built-in limits.

POLICY

How to control NeuronSearchLab

PolicyLayer is an MCP gateway — it sits between your AI agents and NeuronSearchLab, and nothing reaches the server without passing your rules. These are the rules we recommend:

Deny destructive operations

{
  "cancel_training_job": {
    "deny_if": [
      {
        "conditions": [],
        "on_deny": "Blocked by default. Requires approval."
      }
    ]
  }
}

Destructive tools should never be available to autonomous agents without human approval.

Rate limit write operations

{
  "activate_campaign": {
    "limits": [
      {
        "counter": "activate_campaign_per_hour",
        "window": "hour",
        "max": 30,
        "scope": "grant"
      }
    ]
  }
}

Prevents bulk unintended modifications from agents caught in loops.

Cap read operations

{
  "compare_items": {
    "limits": [
      {
        "counter": "compare_items_per_minute",
        "window": "minute",
        "max": 60,
        "scope": "grant"
      }
    ]
  }
}

Controls API costs and prevents retry loops from exhausting upstream rate limits.

Create a free account and register NeuronSearchLab — nothing to install.
Add these rules — paste them, or build them visually. Tune the limits to your setup.
Point your MCP client (Claude, Cursor, anything) at your gateway URL.

ENFORCE POLICY ON NEURONSEARCHLAB →

Instant setup, no code required.

FULL CATALOGUE

All 71 NeuronSearchLab tools

DESTRUCTIVE 9 tools

Destructive cancel_training_job Cancel a running training job. Destructive delete_campaign Delete a campaign and its associated rules. Destructive delete_context Delete a recommendation context. This also removes associated pipelines and rules. Destructive delete_event_type Permanently delete an event type. This cannot be undone. Destructive delete_items Permanently remove one or more items from the recommendation catalogue. Destructive delete_pipeline Delete a ranking pipeline. Destructive delete_rule Delete a ranking rule. Destructive delete_segment Delete a user segment. Any rules referencing this segment will need to be updated. Destructive revoke_api_key Revoke an API key. This immediately invalidates the key and cannot be undone.

EXECUTE 4 tools

Execute activate_pipeline Activate a ranking pipeline so it begins serving ranked results. Execute call_platform_api Fallback tool for internal mode only. Calls safe admin-console /api routes directly when no dedicated MCP tool Execute create_training_job Trigger a new model training job. The job trains on all available interaction data and replaces the current mo Execute start_experiment Start an A/B experiment. Traffic will begin splitting between variants immediately. The experiment moves from

WRITE 27 tools

Write activate_campaign Activate a paused campaign so it starts injecting rules again. Write clone_pipeline Duplicate an existing pipeline with all its stages and configuration. Useful for creating experiment variants Write create_api_key Create a new API key with specified permissions (scopes). The full key is returned once and cannot be retrieve Write create_campaign Create a time-bounded campaign that injects ranking rules between a start and end datetime. Write create_context Create a new recommendation context (feed). Write create_event_type Create a new event type that can be tracked in the recommendation model. Write create_experiment Create a new A/B experiment comparing pipeline variants. Write create_pipeline Create a new ranking pipeline with default stages. Write create_rule Create a new ranking rule. Rule types: Write create_segment Create a new user segment with membership conditions. Write deactivate_pipeline Deactivate a ranking pipeline so it stops serving results without being deleted. Write disable_rule Disable a ranking rule without deleting it. Useful for temporarily pausing rules during debugging or campaigns Write enable_rule Enable a ranking rule that was previously disabled. Write patch_item Partially update an existing item. Write pause_campaign Pause an active campaign, temporarily stopping its rule injections without deleting it. Write reorder_rules Set the priority order of rules by providing rule IDs in the desired order. The first rule in the list gets th Write stop_experiment Stop a running A/B experiment and mark it as completed. Results are preserved for analysis. Write toggle_rule Enable or disable a ranking rule without deleting it. Write track_event Record a user interaction event (click, view, purchase, etc.). Write update_campaign Update a campaign — change its name, description, or start/end dates. Write update_context Update an existing recommendation context. Write update_event_type Update an existing event type\ Write update_experiment Update an experiment that is in draft status. Write update_pipeline Update an existing ranking pipeline. Write update_rule Update an existing ranking rule. Write update_segment Update an existing user segment definition. Write upsert_item Add or update an item in the recommendation catalogue.

READ 31 tools

Read compare_items Compare two items head-to-head by served count, events, clicks, and click-through rate over the same time wind Read explain_ranking Explain why a specific item was (or would be) ranked at a particular position for a user. Read get_auto_recommendations Fetch the next auto-generated recommendation section for a user. Read get_campaign Get the full configuration of a specific campaign including start/end dates and injected rules. Read get_context Get the full configuration of a specific recommendation context by ID. Read get_experiment Get the full configuration of a specific A/B experiment including variants, traffic splits, and current metric Read get_experiment_metrics Get live metrics for a specific A/B experiment broken down by variant — CTR, conversion rate, revenue per sess Read get_experiment_results Get statistical results for a completed or running experiment — per-variant CTR, conversion rate, revenue per Read get_item_analytics Get served counts, event breakdown, watch/click counts, and click-through rate for a specific item over a time Read get_pipeline Get the full configuration of a specific ranking pipeline by ID. Read get_ranking_metrics Get ranking performance metrics for a pipeline or context: CTR by position, overall CTR, conversion rate, dive Read get_recommendations Fetch personalised recommendations for a user. Returns ranked items with scores. Read get_rule Get the full configuration of a specific ranking rule by ID, including conditions and action. Read get_segment Get the full definition of a specific user segment, including all its conditions. Read get_segment_metrics Get ranking performance metrics broken down by user segment — useful for understanding how different user grou Read get_segment_stats Get size and overlap statistics for a user segment — how many users match, percentage of total users, and over Read get_training_job Get details and logs for a specific training job. Read get_user_analytics Get served counts, event breakdown, unique-item activity, and click-through rate for a specific user over a ti Read list_api_keys List all API keys for the team with their names, scopes, environments, and last-used timestamps. Read list_campaigns List all campaigns — time-bounded rule injections that activate on a schedule (e.g. flash sales, seasonal prom Read list_contexts List all recommendation contexts (feeds) configured for your team. Read list_event_types List all configured event types (click, view, purchase, etc.) for the team. Read list_experiments List all A/B experiments with their current status (draft, running, paused, completed). Read list_integrations List configured third-party integrations (webhooks, data connectors, export targets, etc.). Read list_pipelines List all ranking pipelines. Read list_platform_routes List the internal admin-console API routes that the standalone MCP can reach in internal mode. Read list_rules List ranking rules, optionally filtered by context. Read list_segments List all user segments. Segments group users by shared behaviours or attributes and can be referenced in rules Read list_training_jobs List recent model training jobs with their status (running, completed, failed). Training jobs re-train the ran Read search_items Search the recommendation catalogue by keyword. Read top_items List the top items by served count or by matching event activity such as watch or click over a time window. Fo

EXPLORE

Related servers

Other MCP servers with similar tools — same risk classification, starter policies for each.

FAQ

Questions about NeuronSearchLab

Can an AI agent delete data through the NeuronSearchLab MCP server? +

Yes. The NeuronSearchLab server exposes 9 destructive tools including cancel_training_job, delete_campaign, delete_context. These permanently remove resources with no undo. PolicyLayer blocks destructive tools by default so they never reach the upstream server.

How do I prevent bulk modifications through NeuronSearchLab? +

The NeuronSearchLab server has 27 write tools including activate_campaign, clone_pipeline, create_api_key. Set a rate limit in your policy -- for example, 10 calls per hour prevents an agent from making more than 10 modifications per hour. PolicyLayer enforces this at the gateway, before calls reach NeuronSearchLab.

How many tools does the NeuronSearchLab MCP server expose? +

71 tools across 4 categories: Destructive, Execute, Read, Write. 31 are read-only. 40 can modify, create, or delete data.

How do I enforce a policy on NeuronSearchLab? +

Register the NeuronSearchLab MCP server in PolicyLayer, apply the suggested rules above (adjust the limits to your use case), and point your AI client at the PolicyLayer proxy URL instead of the server directly. Your agents keep the same tools; PolicyLayer evaluates every call against policy before it executes. Nothing to install, live in minutes.

Enforce policy on every NeuronSearchLab tool call.

Deterministic rules across all 71 NeuronSearchLab tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

ENFORCE POLICY ON NEURONSEARCHLAB →

Instant setup, no code required.

71 NeuronSearchLab tools catalogued and risk-classified — across an index of 43,000+ MCP servers.