neuronsearchlab

@neuronsearchlab/mcp

MCP (Model Context Protocol) server for NeuronSearchLab. Gives any MCP-compatible AI client (Claude, Codex, Cursor, Windsurf, etc.) direct access to NeuronSearchLab recommendations, product/content search, catalogue operations, analytics, and platform controls.

"Get 5 recommendations for user alice@example.com"
"Check my current plan and show which resources are over their limits"
"Create a new context called Twitter Feed"
"Add a pin rule so Nike items always appear in the top 3"
"Why did item prod-456 rank first for bob?"

Two ways to run it:

• Hosted (recommended, no install): https://console.neuronsearchlab.com/api/mcp — Streamable HTTP with OAuth sign-in or an NSL API key. Listed on the MCP Registry as com.neuronsearchlab/mcp (search neuronsearchlab in the registry API or directory).
• Local stdio via npm: npx -y @neuronsearchlab/mcp in two modes — public (recommendations, events, catalogue via OAuth client credentials) or internal (admin platform via console API key).

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Connect to the hosted server (no install)

The hosted endpoint runs a submission-safe customer administration profile. It includes first-class tools for plan and usage visibility, ranking configuration, experiments, training, analytics, catalogue inspection, API-key inventory and revocation, integrations, and event types. Every hosted tool declares its OAuth requirement and requires the authenticated team's admin scope. Credential creation, billing mutations, and the arbitrary platform API fallback remain available only to trusted local/internal clients so secrets, purchases, and unbounded API calls are not exposed in ChatGPT. Keys minted through OAuth consent appear in console → Security and can be revoked there anytime.

claude.ai / Claude Desktop — Settings → Connectors → Add custom connector → paste https://console.neuronsearchlab.com/api/mcp → Connect, then sign in to your NeuronSearchLab console and approve the scopes.

Claude Code

# OAuth (browser sign-in):
claude mcp add --transport http neuronsearchlab https://console.neuronsearchlab.com/api/mcp
# …or with an API key:
claude mcp add --transport http neuronsearchlab https://console.neuronsearchlab.com/api/mcp \
  --header "Authorization: Bearer nsl_your_key"

OpenAI Codex — in ~/.codex/config.toml:

[mcp_servers.neuronsearchlab]
url = "https://console.neuronsearchlab.com/api/mcp"
bearer_token_env_var = "NSL_API_KEY"

Cursor / Windsurf / other Streamable HTTP clients

{
  "mcpServers": {
    "neuronsearchlab": {
      "url": "https://console.neuronsearchlab.com/api/mcp",
      "headers": { "Authorization": "Bearer nsl_your_key" }
    }
  }
}

---

Resources

• Product site: https://www.neuronsearchlab.com
• MCP setup guide: https://docs.neuronsearchlab.com/sdk/mcp
• AI agents for recommendation operations: https://www.neuronsearchlab.com/blog/ai-agents-for-recommendation-operations
• Recommendation systems reading path: https://www.neuronsearchlab.com/blog/recommendation-systems

Tools

API tools

Tool	Description
get_recommendations	Fetch personalised recommendations for a user
get_auto_recommendations	Auto-sectioned feed with pagination (infinite scroll)
track_event	Record a user interaction (click, view, purchase, etc.)
upsert_item	Add or update a catalogue item
patch_item	Partially update an item (enable/disable, change fields)
delete_items	Permanently remove items from the catalogue
search_items	Search the catalogue by keyword
explain_ranking	Explain why an item ranked where it did for a user

Modes

Public mode

Uses OAuth client credentials and the public API.

Supported:

• recommendations
• events
• catalogue operations

Internal mode

Uses a NeuronSearchLab API key with the admin scope against the console API.

Currently supported:

• account plan and limits: get_account_plan returns the effective plan, resolved limits, current usage, and exact overages
• catalogue search and ranking debug: search_items, explain_ranking
• contexts: list_contexts, create_context, update_context, delete_context, get_context
• pipelines: list_pipelines, create_pipeline, update_pipeline, delete_pipeline, activate_pipeline, deactivate_pipeline, clone_pipeline, get_pipeline
• rules: list_rules, create_rule, update_rule, delete_rule, toggle_rule, enable_rule, disable_rule, get_rule
• segments: list_segments, get_segment, create_segment, update_segment, delete_segment
• experiments: list_experiments, get_experiment, create_experiment, update_experiment, start_experiment, stop_experiment, get_experiment_results, refresh_experiment_results
• training: list_training_jobs, get_training_job, create_training_job, cancel_training_job
• analytics: get_ranking_metrics, get_user_analytics, get_item_analytics, compare_items, top_items
• event types: list_event_types, create_event_type, update_event_type, delete_event_type
• credentials and integrations: list_api_keys, revoke_api_key, list_integrations (create_api_key is local/internal only because it returns credential material)
• fallback UI coverage for trusted local/internal clients only: list_platform_routes, call_platform_api

---

Quickstart (local stdio)

1. Get credentials

Generate SDK Credentials (OAuth 2.0 client ID + secret) from the NeuronSearchLab console.

2. Add to Claude Desktop

Public mode (recommendations, events, catalogue):

Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "neuronsearchlab": {
      "command": "npx",
      "args": ["-y", "@neuronsearchlab/mcp"],
      "env": {
        "NSL_CLIENT_ID": "your-client-id",
        "NSL_CLIENT_SECRET": "your-client-secret"
      }
    }
  }
}

Internal mode (admin platform — contexts, pipelines, rules, analytics, etc.):

{
  "mcpServers": {
    "neuronsearchlab": {
      "command": "npx",
      "args": ["-y", "@neuronsearchlab/mcp"],
      "env": {
        "NSL_PLATFORM_MODE": "internal",
        "NSL_API_KEY": "your-admin-api-key"
      }
    }
  }
}

Restart Claude Desktop. You'll see a 🔌 neuronsearchlab indicator in the toolbar when it's connected.

Try it: recommendation-ops demo

Once connected, run this named demo path before wiring the server into a larger workflow. It proves that an AI client can operate the recommender as an investigation surface rather than just call a recommendation endpoint.

1. List contexts so the assistant confirms the exact surface it is about to inspect.
2. Fetch recommendations for a known test user and context.
3. Search the catalogue for a concrete product/content phrase and compare the returned item IDs with the recommendation set.
4. Explain one ranked item using the request_id from the recommendation or search response when available.
5. Optional, sandbox only: draft or toggle a rule after the explanation shows the expected leak. Keep production contexts read-only unless the operator explicitly approves a write.

Use the NeuronSearchLab MCP server in read-only mode first.
List my recommendation contexts and choose the homepage-feed context.
Get 10 recommendations for user demo-user@example.com using context homepage-feed.
Search the catalogue for waterproof jackets and show the top 5 item ids.
Explain why the first recommended item ranked first, using the request_id from the recommendation response if available.
If you see a relevance leak, draft the smallest rule that would fix it, but do not create or toggle the rule yet.

Internal/admin mode can also inspect operational state before making changes:

List ranking rules for the homepage-feed context.
Show the latest ranking metrics for that context.
Compare item jacket-123 with item jacket-456 and explain which rule or signal separates them.

Next steps after the smoke test:

• create a scoped API key for the client or MCP server
• connect one real recommendation context, such as homepage-feed
• add request attribution to click/view events before judging ranking quality

3. Cursor / other MCP clients

Follow your client's MCP server guide. The command is:

npx @neuronsearchlab/mcp

Set NSL_CLIENT_ID + NSL_CLIENT_SECRET for public mode, or NSL_PLATFORM_MODE=internal + NSL_API_KEY for internal mode.

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Releases

This repo uses Changesets plus GitHub Actions for automated versioning and npm publishing.

• Add a changeset for any user-facing package change with npm run changeset
• Merge that PR into main
• The release.yml workflow opens or updates a version PR
• Merging the version PR publishes @neuronsearchlab/mcp to npm automatically

To enable trusted publishing, configure the package on npmjs.com to trust the release.yml workflow in this repository.

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Configuration

All configuration is via environment variables:

Variable	Required	Default	Description
NSL_PLATFORM_MODE	No	public	public or internal
NSL_CLIENT_ID	Public mode	—	OAuth client ID from the console
NSL_CLIENT_SECRET	Public mode	—	OAuth client secret from the console
NSL_API_KEY	Internal mode	—	API key with admin scope
NSL_TOKEN_URL	No	https://auth.neuronsearchlab.com/oauth2/token	Token endpoint
NSL_API_BASE_URL	No	https://api.neuronsearchlab.com in public mode, https://console.neuronsearchlab.com in internal mode	API base URL
NSL_TIMEOUT_MS	No	15000	Request timeout in milliseconds

---

Tool reference

get_recommendations

Fetch personalised recommendations for a user. Returns ranked items with scores and a request_id for attribution.

Inputs

Field	Type	Required	Description
user_id	string	Yes	User identifier (UUID, email, or any stable string)
context_id	positive integer	No	Context ID generated when the context is created in the console
limit	integer 1–200	No	Number of items to return (defaults to context value, usually 20)
surface	string	No	Rerank surface override (e.g. "homepage", "sidebar")

Example

Get 10 recommendations for user alice@example.com using context 101

---

get_auto_recommendations

Fetch the next auto-generated section for a user's feed. Designed for infinite-scroll — each call returns one curated section (e.g. "Trending this week", "New for you") plus a cursor for the next section. Call until done: true.

Inputs

Field	Type	Required	Description
user_id	string	Yes	User identifier
context_id	positive integer	No	Optional console-generated context ID
limit	integer 1–200	No	Items per section
cursor	string	No	Pagination cursor from the previous response
window_days	integer	No	Days to look back for "new" content

---

track_event

Record a user interaction. Always pass request_id from the recommendations response to enable click-through attribution.

Inputs

Field	Type	Required	Description
event_id	integer	Yes	Numeric event type ID from the admin console
user_id	string	Yes	User who triggered the event
item_id	positive integer	Yes	NSL-generated item ID returned by ingestion
request_id	string	No	request_id from the recommendations response (for attribution)
session_id	string	No	Session identifier for grouping events within a visit

---

upsert_item

Add or update an item in the catalogue. The description field is used to generate the embedding — write it to be rich and descriptive.

Inputs

Field	Type	Required	Description
name	string	Yes	Display name
description	string	Yes	Rich description for embedding generation
metadata	object	No	Arbitrary key-value pairs returned with recommendations

The item ID is generated by NSL and returned in the ingestion response; callers cannot supply it.

---

patch_item

Partially update an existing catalogue item.

Inputs

Field	Type	Required	Description
item_id	positive integer	Yes	NSL-generated item ID to update
active	boolean	No	false to exclude from recommendations without deleting

---

delete_items

Permanently remove items. Cannot be undone. To temporarily exclude, use patch_item with active: false.

Inputs

Field	Type	Required	Description
item_ids	positive integer[] (max 100)	Yes	NSL-generated item IDs to delete

---

search_items

Search the catalogue by keyword.

Inputs

Field	Type	Required	Description
query	string	Yes	Text to search for
limit	integer 1–100	No	Max results (default 20)

---

explain_ranking

Explain why a specific item was ranked at a given position for a user. Returns score breakdown, applied rules, and pipeline trace.

Inputs

Field	Type	Required	Description
item_id	positive integer	Yes	NSL-generated item ID to explain
user_id	string	No	User to score against (omit for neutral baseline)
context_id	positive integer	No	Console-generated context ID to apply scoring rules from

---

list_contexts

List all recommendation contexts (feeds) configured for your team.

Inputs — none

---

create_context

Create a new recommendation context.

Inputs

Field	Type	Required	Description
context_name	string	Yes	Display name (e.g. "Twitter Feed")
context_key	string	No	URL-safe key (auto-derived from name)
context_type	enum	No	homepage_feed, you_may_also_like, item_detail_related, search_assist, campaign_merchandising. Default: homepage_feed
description	string	No	Optional description
recommendation_type	enum	No	item_to_item, item_to_user, user_to_item, user_to_user. Default: user_to_item

Example

Create a new context called "Twitter Feed" with type homepage_feed

---

update_context

Update an existing context.

Inputs

Field	Type	Required	Description
context_id	integer	Yes	The context ID to update
context_name	string	No	New display name
context_type	enum	No	New context type
description	string	No	New description
recommendation_type	enum	No	New recommendation type

---

delete_context

Permanently delete a context and its attached pipelines, rules, and feed blueprints.

Inputs

Field	Type	Required	Description
context_id	integer	Yes	The context ID to delete

---

list_pipelines

List all ranking pipelines.

Inputs — none

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create_pipeline

Create a new ranking pipeline with default stages.

Inputs

Field	Type	Required	Description
name	string	Yes	Pipeline name
description	string	No	Optional description
context_id	integer	No	Context to attach this pipeline to
is_active	boolean	No	Default: true

---

update_pipeline / delete_pipeline

Update or delete a pipeline by pipeline_id.

---

list_rules

List ranking rules, optionally filtered by context_id.

---

create_rule

Create a ranking rule. Rule types:

Type	Effect
boost	Increase matching items' scores (use weight 1.0–5.0)
bury	Decrease matching items' scores (use weight 0.0–1.0)
pin	Fix matching items at a specific position (use pin_position)
filter	Remove matching items from results
cap	Limit matching items to a fraction of results (use cap_fraction)
diversity	Spread items across a field's values (use diversity_field, diversity_max)

Inputs

Field	Type	Required	Description
name	string	Yes	Rule display name
rule_type	enum	Yes	boost, bury, pin, filter, cap, diversity
conditions	array	Yes	[{ field, operator, value }] — items must match all conditions
actions	object	Yes	{ type, weight?, pin_position?, cap_fraction?, ... }
context_id	integer	No	Scope rule to a specific context
description	string	No	Optional description
priority	integer 0–1000	No	Higher = evaluated first. Default: 100

Example

Create a pin rule called "Pin Nike" that pins items where brand equals "Nike" to position 3, scoped to context 1

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update_rule / delete_rule / toggle_rule / enable_rule / disable_rule

Update, delete, or enable/disable a rule by rule_id.

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get_user_analytics

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

Inputs

Field	Type	Required	Description
user_id	string	Yes	User ID or email to inspect
context_id	positive integer	No	Scope to a console-generated context
window	1d | 7d | 30d | 90d	No	Time window (default 7d)

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get_item_analytics

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

Inputs

Field	Type	Required	Description
item_id	positive integer	Yes	NSL-generated item ID to inspect
context_id	positive integer	No	Scope to a console-generated context
window	1d | 7d | 30d | 90d	No	Time window (default 7d)

---

compare_items

Compare two items head-to-head by served count, events, clicks, and CTR over the same time window.

Inputs

Field	Type	Required	Description
item_a_id	positive integer	Yes	First NSL-generated item ID
item_b_id	positive integer	Yes	Second NSL-generated item ID
context_id	positive integer	No	Scope to a console-generated context
window	1d | 7d | 30d | 90d	No	Time window (default 7d)

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top_items

List the top items by served count or by matching event activity over a time window. Use metric="served" for generic "top item" or "best performing" questions. Use metric="events" when the user explicitly names an engagement signal (e.g. watch, click, purchase).

Inputs

Field	Type	Required	Description
metric	served | events	No	Rank by served count or event count (default served)
event_name	string	No	Event name filter when metric=events (e.g. "watch", "click")
event_id	integer	No	Numeric event ID filter when metric=events
context_id	positive integer	No	Scope to a console-generated context
window	1d | 7d | 30d | 90d	No	Time window (default 7d)
limit	integer 1–50	No	Max items to return (default 10)

Example

What's the top item served in the last 7 days?
Which items had the most watch events last month?

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Authentication

Public mode uses OAuth 2.0 Client Credentials. Tokens are fetched on startup, cached in memory, and auto-refreshed 60 seconds before expiry.

Internal mode uses a NeuronSearchLab API key with the admin scope. Set NSL_API_KEY and NSL_PLATFORM_MODE=internal.

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Development

git clone https://github.com/NeuronSearchLab/mcp
cd mcp
npm install
export NSL_CLIENT_ID=your-client-id
export NSL_CLIENT_SECRET=your-client-secret
npm run dev           # dev mode (tsx, no build)
npm run build         # compile to dist/

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License

MIT