NPS Intelligence By Customergauge

Created By
CustomerGauge23 days ago
Query ~3,800 publicly disclosed Net Promoter NPS scores from named companies (2011–present), with citations to original sources. Pre-computed sector benchmarks included.
Overview

.stats { display: grid; grid-template-columns: repeat(auto-fit, minmax(160px, 1fr)); gap: 14px; margin-bottom: 40px; } .stat { background: var(--card); border: 1px solid var(--border); border-radius: 8px; padding: 16px 18px; } .stat-n { font-size: 26px; font-weight: 700; color: var(--gold-dim); line-height: 1; } .stat-l { font-size: 10px; color: var(--muted); margin-top: 6px; font-family: ui-monospace, 'SF Mono', Menlo, Consolas, monospace; letter-spacing: 0.9px; text-transform: uppercase; }

h2 { font-size: 11px; text-transform: uppercase; letter-spacing: 1.5px; color: var(--gold-dim); font-family: ui-monospace, 'SF Mono', Menlo, Consolas, monospace; margin: 36px 0 14px; font-weight: 700; } .card { background: var(--card); border: 1px solid var(--border); border-radius: 8px; padding: 18px 22px; margin-bottom: 14px; } .card ul { padding-left: 20px; } .card li { margin: 4px 0; } .card li code { background: var(--gold-bg); color: var(--gold-dim); padding: 1px 6px; border-radius: 4px; font: 13px ui-monospace, 'SF Mono', Menlo, Consolas, monospace; }

.questions { display: grid; gap: 8px; } .q { background: var(--card); border: 1px solid var(--border); border-left: 3px solid var(--gold); border-radius: 4px; padding: 12px 16px; font-size: 15px; color: var(--text); font-style: italic; } .q::before { content: '"'; color: var(--gold); font-weight: 700; } .q::after { content: '"'; color: var(--gold); font-weight: 700; }

pre { background: var(--black); color: var(--gold); border-radius: 6px; padding: 12px 16px; overflow-x: auto; font: 13px ui-monospace, 'SF Mono', Menlo, Consolas, monospace; margin: 8px 0; } .connect-row { display: flex; gap: 24px; flex-wrap: wrap; } .connect-col { flex: 1 1 320px; } .connect-col h3 { font-size: 13px; font-weight: 700; color: var(--text); margin-bottom: 8px; } .connect-col p { font-size: 14px; color: var(--muted); margin-bottom: 8px; }

footer { max-width: 880px; margin: 32px auto 0; padding: 24px 32px; border-top: 1px solid var(--border); color: var(--muted); font-size: 13px; } footer a { color: var(--gold-dim); text-decoration: none; font-weight: 600; } footer a:hover { text-decoration: underline; }

@media (max-width: 600px) { .brand-block { flex-direction: column; align-items: flex-start; gap: 10px; } .header-cta { width: 100%; } main { padding: 24px 20px 48px; } }

NPS Intelligence — MCP

Public Net Promoter Score (NPS) disclosures · Live, citable, in Claude

A remote MCP server that lets Claude (and any other MCP-aware client) query a curated database of publicly disclosed Net Promoter Score (NPS) values from named companies — with source URLs, news snippets, and pre-computed sector benchmarks across 1,000+ industries.

3,806
NPS Disclosures
1,097
Sector Benchmarks
15 yrs
2011 – Today
~few days
Refresh Cadence

Try asking Claude

What has Salesforce publicly disclosed about NPS?
Show me Banking NPS disclosures since 2023, highest first.
What's the median publicly disclosed NPS in Insurance? How many companies?
Which SaaS companies have the highest publicly disclosed NPS, and where did they publish it?
Has Tesla disclosed an NPS? Show me the trend by year.
What does this database cover? How far back does it go?

Tools exposed

  • lookup_company — every disclosed NPS for a named company, with citations
  • search_disclosures — filter by sector, year range, and quality
  • get_sector_benchmark — pre-computed mean / median / p75 / min / max per sector
  • list_coverage — what the database actually contains

Connect

Claude.ai (browser)

Settings → Connectors → Add custom connector.

https://cg-nps-mcp.adam-dorrell.workers.dev/mcp

Claude Desktop (Mac/Win)

Uses mcp-remote proxy with the /sse endpoint.

https://cg-nps-mcp.adam-dorrell.workers.dev/sse

Power users — install the skill

The MCP server ships its own instructions automatically — every client that connects gets the citation rules, tool guidance, and disclosure-bias framing without any extra install. But if you want the same rules applied locally (e.g. when working with an exported CSV), grab the skill as a markdown file:

curl -o ~/.claude/skills/cg-nps/SKILL.md https://cg-nps-mcp.adam-dorrell.workers.dev/skill

Why this is interesting

Claude doesn't know what specific companies have published about their NPS. This MCP gives it 15 years of curated public disclosures with a source URL on every row — so any answer Claude gives can be verified, and any company named is grounded in a real news article or investor document. It's the evidence layer that turns "what's a typical NPS?" from a guess into a citation.

Project Info
Created At
23 days ago
Updated At
6 hours ago
Author Name
CustomerGauge
Star
-
Language
-
License
-
Category

Recommend Servers

View All
Mnemom

14 hours ago
//beforeyouship — LLM Cost Modeling From Your Editor
@Indiegoing

Query realistic LLM cost models without leaving your editor. beforeyouship models the **true monthly cost** of an LLM app architecture — retries, prompt caching, batch discounts, infra overhead, and 3×/10× growth — across GPT-5.x, Claude, Gemini, DeepSeek, and more. Not a token calculator: a planning tool for the design phase, before you commit to a stack. **No API key needed to try it** — demo mode covers the six free-tier models. A Pro key from [beforeyouship.dev](https://beforeyouship.dev) unlocks the full 18-model catalog. ## What you can ask - "How much will a RAG chatbot cost at 10,000 requests/day?" - "Compare Claude Haiku vs Gemini Flash pricing for my workload" - "What's the cheapest model for a multi-step agent at scale?" - "Show me current per-token prices for Anthropic models" ## Tools ### `estimate_cost` Full cost model for an architecture at a given usage level. Returns Naive / Realistic / Worst Case monthly cost per model, 3×/10× growth scenarios, and an opinionated recommendation with reasoning. ### `get_model_prices` Current per-1M-token pricing — input, output, cached input, batch — with context windows and staleness metadata. ### `list_archetypes` Seven preset architecture patterns (simple chatbot, chatbot with history, RAG pipeline, multi-model router, coding assistant, document processor, multi-step agent) used as starting points for estimates. ## Setup **Claude Code:** ​```bash claude mcp add --transport http beforeyouship https://beforeyouship.dev/api/mcp ​``` **Cursor / other clients** — add a remote server: ​```json { "mcpServers": { "beforeyouship": { "type": "streamable-http", "url": "https://beforeyouship.dev/api/mcp" } } } ​``` Add an `Authorization: Bearer bys_...` header with a Pro key for the full catalog. ## Try it > Estimate the monthly cost of a RAG pipeline at 10,000 requests/day

13 hours ago
Docwand

13 hours ago