MCP Server for Z.AI - A Model Context Protocol server that provides AI capabilities
Nothing to run from here
Neuronsearchlab is installed from its publisher's own source and answers where it runs, so this marketplace is not in the path of a single call. There is no address here to send one to, and a panel that pretended otherwise would be showing you an answer we made up. Install it and call it from your own client — the Installation tab has the entry for each one.
100/100
trust score
9
downloads a day
local
runs on your machine
543
downloads in 30d
What it does
MCP server for NeuronSearchLab recommendations, search, catalogue, analytics, and platform operations.
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user neuronsearchlab-mcp '--' npx -y @neuronsearchlab/mcp
# 2 — ask your agent for something these tools do
# get_recommendations, get_auto_recommendations, track_event and 70 more
Neuronsearchlab costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publishernot claimed
NEAll 1 server
neuronsearchlab1 server
1 category
Collected from a public index. Nobody has claimed this account, so nothing here was written by its author — claim it if it is yours.
Installation
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. This one runs on your machine: your client starts Neuronsearchlab as a process under your own user, with your files and your network, so the tool surface below is what it can do to your computer rather than to a server somewhere else. Its scan result is on the Security tab — read the surface before you approve it.
Claude Codedeclared by this listing
This is a public server: you run it yourself and this marketplace is not in the path. You need Node.js — the current LTS (24) is the safe choice; npx comes with it. Claude Code registers it in one command; --scope user makes it available in every project. The '--' is in quotes so that PowerShell passes it on.
claude mcp add --scope user neuronsearchlab-mcp '--' npx -y @neuronsearchlab/mcpReconnect, or start a new session, and the tools appear in the model’s tool list.
Free on mcprush, and it runs from its publisher’s own package: this marketplace is not in the path, so there is no key of ours to hold and no card to add.
What it asks of you
Nothing is required, but it can take NSL_CLIENT_ID (OAuth client ID for public mode (SDK Credentials from console → Security).), NSL_CLIENT_SECRET (OAuth client secret for public mode.), NSL_PLATFORM_MODE (Set to 'internal' for admin-platform tools with an API key; defaults to 'public'.), NSL_API_KEY (NSL API key (nsl_...) for internal mode.) if you want to set them.
What it writes
One config entry your client uses to start the process locally. A local server runs with your file system and your network, which is why it is priced without metering.
What you approve
73 tools, with what each one reads, writes and reaches shown before you agree — the same list on every route above. Read the tool surface.
Tool surface
What the model actually sees. Descriptions are diffed on every release — see version history.
Fetch personalised recommendations for a user. Returns ranked items with scores.
No parameter schema on file.
Fetch the next auto-generated recommendation section for a user.
No parameter schema on file.
Record a user interaction event (click, view, purchase, etc.).
No parameter schema on file.
Add or update an item in the recommendation catalogue.
No parameter schema on file.
Partially update an existing item.
No parameter schema on file.
Permanently remove one or more items from the recommendation catalogue.
No parameter schema on file.
Search the recommendation catalogue by keyword.
No parameter schema on file.
Explain why a specific item was (or would be) ranked at a particular position for a user.
No parameter schema on file.
Get the authenticated team\
No parameter schema on file.
List all recommendation contexts (feeds) configured for your team.
No parameter schema on file.
Create a new recommendation context (feed).
No parameter schema on file.
Update an existing recommendation context.
No parameter schema on file.
Delete a recommendation context. This also removes associated pipelines and rules.
No parameter schema on file.
List all ranking pipelines.
No parameter schema on file.
Create a new ranking pipeline with default stages.
No parameter schema on file.
Update an existing ranking pipeline.
No parameter schema on file.
Delete a ranking pipeline.
No parameter schema on file.
List ranking rules, optionally filtered by context.
No parameter schema on file.
Create a new ranking rule. Rule types:
No parameter schema on file.
Update an existing ranking rule.
No parameter schema on file.
Delete a ranking rule.
No parameter schema on file.
Enable or disable a ranking rule without deleting it.
No parameter schema on file.
Get the full configuration of a specific recommendation context by ID.
No parameter schema on file.
Get the full configuration of a specific ranking pipeline by ID.
No parameter schema on file.
Activate a ranking pipeline so it begins serving ranked results.
No parameter schema on file.
Deactivate a ranking pipeline so it stops serving results without being deleted.
No parameter schema on file.
Duplicate an existing pipeline with all its stages and configuration. Useful for creating experiment variants or safe copies before making changes.
No parameter schema on file.
Get the full configuration of a specific ranking rule by ID, including conditions and action.
No parameter schema on file.
Enable a ranking rule that was previously disabled.
No parameter schema on file.
Disable a ranking rule without deleting it. Useful for temporarily pausing rules during debugging or campaigns.
No parameter schema on file.
Set the priority order of rules by providing rule IDs in the desired order. The first rule in the list gets the highest priority and is evaluated first.
No parameter schema on file.
List all user segments. Segments group users by shared behaviours or attributes and can be referenced in rules and campaigns.
No parameter schema on file.
Get the full definition of a specific user segment, including all its conditions.
No parameter schema on file.
Create a new user segment with membership conditions.
No parameter schema on file.
Update an existing user segment definition.
No parameter schema on file.
Delete a user segment. Any rules referencing this segment will need to be updated.
No parameter schema on file.
Get size and overlap statistics for a user segment — how many users match, percentage of total users, and overlap with other segments.
No parameter schema on file.
List all A/B experiments with their current status (draft, running, paused, completed).
No parameter schema on file.
Get the full configuration of a specific A/B experiment including variants, traffic splits, and current metrics.
No parameter schema on file.
Create a new A/B experiment comparing pipeline variants.
No parameter schema on file.
Update an experiment that is in draft status.
No parameter schema on file.
Start an A/B experiment. Traffic will begin splitting between variants immediately. The experiment moves from draft to running status.
No parameter schema on file.
Stop a running A/B experiment and mark it as completed. Results are preserved for analysis.
No parameter schema on file.
Get the most recently calculated statistical results for a completed or running experiment without changing stored data.
No parameter schema on file.
Recalculate experiment metrics from current recommendation and event data, store the refreshed metrics, and return the updated experiment results.
No parameter schema on file.
List all campaigns — time-bounded rule injections that activate on a schedule (e.g. flash sales, seasonal promotions).
No parameter schema on file.
Get the full configuration of a specific campaign including start/end dates and injected rules.
No parameter schema on file.
Create a time-bounded campaign that injects ranking rules between a start and end datetime.
No parameter schema on file.
Update a campaign — change its name, description, or start/end dates.
No parameter schema on file.
Delete a campaign and its associated rules.
No parameter schema on file.
Activate a paused campaign so it starts injecting rules again.
No parameter schema on file.
Pause an active campaign, temporarily stopping its rule injections without deleting it.
No parameter schema on file.
List recent model training jobs with their status (running, completed, failed). Training jobs re-train the ranking model on new interaction data.
No parameter schema on file.
Get a customer-facing status summary for a specific training job without returning infrastructure identifiers or raw logs.
No parameter schema on file.
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.
No parameter schema on file.
Cancel a running training job.
No parameter schema on file.
Get ranking performance metrics for a pipeline or context: CTR by position, overall CTR, conversion rate, diversity score, catalogue coverage, and revenue per session.
No parameter schema on file.
Get live metrics for a specific A/B experiment broken down by variant — CTR, conversion rate, revenue per session, and statistical significance.
No parameter schema on file.
Get ranking performance metrics broken down by user segment — useful for understanding how different user groups respond to your ranking configuration.
No parameter schema on file.
Get served counts, event breakdown, unique-item activity, and click-through rate for a specific user over a time window.
No parameter schema on file.
Get served counts, event breakdown, watch/click counts, and click-through rate for a specific item over a time window.
No parameter schema on file.
Compare two items head-to-head by served count, events, clicks, and click-through rate over the same time window.
No parameter schema on file.
List the top items by served count or by matching event activity such as watch or click over a time window. For generic
No parameter schema on file.
List all API keys for the team with their names, scopes, environments, and last-used timestamps.
No parameter schema on file.
Create a new API key with specified permissions (scopes). The full key is returned once and cannot be retrieved again.
No parameter schema on file.
Revoke an API key. This immediately invalidates the key and cannot be undone.
No parameter schema on file.
List configured third-party integrations (webhooks, data connectors, export targets, etc.).
No parameter schema on file.
List all configured event types (click, view, purchase, etc.) for the team.
No parameter schema on file.
Create a new event type that can be tracked in the recommendation model.
No parameter schema on file.
Update an existing event type\
No parameter schema on file.
Permanently delete an event type. This cannot be undone.
No parameter schema on file.
List the internal admin-console API routes that the standalone MCP can reach in internal mode.
No parameter schema on file.
Fallback tool for internal mode only. Calls safe admin-console /api routes directly when no dedicated MCP tool exists.
No parameter schema on file.
Free
$0 forever
- Every tool, no call limit
- No card, and mcprush charges nothing for it
- Source published under MIT
- Runs on your machine — nothing of it reaches our gateway
- Nothing to cap, because mcprush bills nothing
What counts against your monthly calls
| Tool | Unit | Calls used | Out of the allowance |
|---|
Nothing here is billable: mcprush charges nothing to install Neuronsearchlab or to call it, at any volume.
Call allowance · this installenforced at the gateway
Monthly allowance, in calls
—
—
Alert at 80% · then the tool returns a normal MCP error
Used this month: —
ATrust score 100 / 100
Release 1.2.0 · scanned 14 Oct 2026
Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.
Threat score100
Client score100
Findings0 threat · 0 capability
Capabilityminimal
Coveragesource
Verificationsource
Surface digestdd5254bd6eca7942c438369b861a787ab16a7ed960913bad45d073e2ef3e82e5
Score vectorthreat 100 → client 100
| Term | Level | What it prices | Points |
|---|---|---|---|
| capability-exposure | minimal | capability blast radius (minimal) — client exposure if the model is manipulated | −0 |
| verification-discount | source | publisher verification (provenance) — cryptographic build provenance ties the artifact to its source | −0 |
| coverage-honesty | source | inspection depth (source) — how much of the target the scan could see | −0 |
score = clamp(round(threatScore − E_cap − E_ver − E_cov), 0, 100). No model, no opinion, no network call at scoring time: the same methodology version against the same bytes always produces this number, and every line above is a field of the report rather than a summary of it.
What the scan could actually read
A grade is only as meaningful as its coverage, so the scanner publishes its own depth before it publishes its result.
Tool surfaceread
Implementation sourceread
Package metadataread
Live transportnot available
Tools were statically extracted from the published source (73 recovered), not enumerated from a running server. Tool-poisoning, Unicode-smuggling, capability and toxic-flow analysis ran on this inferred surface, but a mis-parsed registration could be missed or mis-attributed, so tool-derived findings are capped below “confirmed”. To grade the real runtime surface, scan the running server: --command "npx -y <package>".
Capability — what it could do if the model were manipulated
Tags derived from each tool’s schema and the implementation, not from what the tool calls itself. minimal is the level these add up to.
| Tool | Capability tags | Why the tag was assigned |
|---|---|---|
| get_recommendations | untrusted-input | untrusted-input: keyword "fetch" |
| get_auto_recommendations | untrusted-input | untrusted-input: keyword "fetch" |
| track_event | no tags | |
| upsert_item | no tags | |
| patch_item | no tags | |
| delete_items | no tags | |
| search_items | no tags | |
| explain_ranking | no tags | |
| get_account_plan | no tags | |
| list_contexts | no tags | |
| create_context | no tags | |
| update_context | no tags | |
| delete_context | no tags | |
| list_pipelines | no tags | |
| create_pipeline | no tags | |
| update_pipeline | no tags | |
| delete_pipeline | no tags | |
| list_rules | no tags | |
| create_rule | no tags | |
| update_rule | no tags | |
| delete_rule | no tags | |
| toggle_rule | no tags | |
| get_context | no tags | |
| get_pipeline | no tags | |
| activate_pipeline | no tags | |
| deactivate_pipeline | no tags | |
| clone_pipeline | no tags | |
| get_rule | no tags | |
| enable_rule | no tags | |
| disable_rule | no tags | |
| reorder_rules | no tags | |
| list_segments | no tags | |
| get_segment | no tags | |
| create_segment | no tags | |
| update_segment | no tags | |
| delete_segment | no tags | |
| get_segment_stats | no tags | |
| list_experiments | no tags | |
| get_experiment | no tags | |
| create_experiment | no tags | |
| update_experiment | no tags | |
| start_experiment | no tags | |
| stop_experiment | no tags | |
| get_experiment_results | no tags | |
| refresh_experiment_results | no tags | |
| list_campaigns | no tags | |
| get_campaign | no tags | |
| create_campaign | no tags | |
| update_campaign | no tags | |
| delete_campaign | no tags | |
| activate_campaign | no tags | |
| pause_campaign | no tags | |
| list_training_jobs | no tags | |
| get_training_job | no tags | |
| create_training_job | no tags | |
| cancel_training_job | no tags | |
| get_ranking_metrics | no tags | |
| get_experiment_metrics | no tags | |
| get_segment_metrics | no tags | |
| get_user_analytics | no tags | |
| get_item_analytics | no tags | |
| compare_items | no tags | |
| top_items | no tags | |
| list_api_keys | no tags | |
| create_api_key | no tags | |
| revoke_api_key | no tags | |
| list_integrations | no tags | |
| list_event_types | no tags | |
| create_event_type | no tags | |
| update_event_type | no tags | |
| delete_event_type | no tags | |
| list_platform_routes | no tags | |
| call_platform_api | no tags |
Toxic-flow graph
The lethal trifecta, checked as a graph rather than as a checklist: untrusted input, a sensitive source and an external sink have to meet before there is a path worth worrying about.
The public result for this release does not print the flow graph, so there is nothing to show here. That is not the same as "no paths were found": what the scan did read is above, under coverage.
Supply chain and provenance
Integrityfirst-seen
Surface digestdd5254bd6eca7942
Scanned2026-10-14
This is the first scan of this surface here, so there is nothing yet to compare it against.
Every result on this tab comes from one deterministic pass over the published package — offline, rule by rule, and auditable line by line above. Same methodology version, same bytes, same score.
Release history
Always latest — the install command names no version, so it installs the newest release when it runs; the current release on file is 1.2.0. Pin a release below and the command asks for exactly that one.
1.2.0
23 Sep 2026
latestNo release note was published with this version.
1.1.0
undated
- The version this listing was on when mcprush began following its releases. The registry was not asked when it shipped, so this row carries no date.
—
no reviews yet
Who can review
One review per account, from a signed-in account that does not publish this listing. Nothing else is asked — you can say what you think before you install it. Publishers can reply once per review, and a review can be edited or deleted by whoever wrote it.
Sign in to write one
Any signed-in account that does not publish Neuronsearchlab can review it, once — before installing it or after. A review from an account that has installed it is marked as one.
No reviews yet
Nobody has reviewed this listing. The rating on the card is the mean of the reviews written here and nothing else, so there is no rating until somebody writes the first — which takes a signed-in account that does not publish it, and nothing else.