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MCP Analytics

by embeddedlayers

Analyze business data through chat with statistical methods, forecasts, ML models, connectors, and repeatable HTML reports.

Install MCP Analytics

Pick your client, then add it in one click or paste the config.

Streamable HTTP (Remote)
Server URL
https://api.mcpanalytics.ai/mcp/api-key

Remote servers are added in the app, not the config file: Customize → Connectors → Add custom connector, then paste this URL.

Overview

embeddedlayers/mcp-analytics connects AI clients to uploaded datasets and live business data for statistical analysis, forecasting, and machine learning. It uses a resolver to choose between a direct answer, a library analysis, or a custom analysis, then returns a report with charts, tables, interpretation, named methods, and execution code. Data can come from CSV uploads or supported sources such as Shopify, Stripe, WooCommerce, eBay, GA4, and Search Console. Reach for it when business questions require reproducible analysis rather than general-purpose chat.

Use cases

  • Upload sales data and explain changes in revenue by channel
  • Forecast next-quarter sales from a CSV file
  • Identify customers with a high likelihood of churn
  • Query connected commerce and analytics data
  • Repeat an analysis on a later dataset

Key features

  • CSV dataset uploads and listing
  • Queries for connected business data sources
  • Statistical analysis, forecasting, and machine learning
  • Resolver for direct, library, or custom analysis paths
  • Charts, tables, interpretations, methods, and executed code in reports
  • Repeatable analyses and searchable report libraries

Tools18

What MCP Analytics lets your agent do. Expand a tool for its parameters.

Self-reported~56 tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 2d ago

Recent health check succeeded.

18/18 checks healthy over the last 80d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 9d ago via OSV.dev · https://api.mcpanalytics.ai/mcp/api-key (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars8Stargazers on the source repository.
npm downloads24Package downloads in the last 30 days.
Last commit4d agoMost recent push to the default branch.
Tools exposed18Callable tools this server registers over MCP.
Directory activity5 viewsConfig copies, upvotes, and views on AllMCPs.

How MCP Analytics works

What embeddedlayers/mcp-analytics does

embeddedlayers/mcp-analytics turns questions about business data into analysis workflows accessed through MCP. It can provide quick answers based on the full dataset, run established statistical methods, or build a custom analysis for questions specific to a business. The result is a report containing visualizations, tables, written interpretation, the method used, and the code executed.

Analyses are designed to be repeatable. After an analysis has been created, the same workflow can be run against a later file, such as the next month’s data. Reports can also be stored, listed, viewed, and queried as a library.

How it works

The server is exposed through a Streamable HTTP JSON-RPC endpoint:

https://api.mcpanalytics.ai/mcp/api-key

A first request acts as a resolver. It examines the question and available data without executing an analysis or charging for that step, then identifies the suitable route and explains why. The selected route can be a straightforward answer, a library analysis, or a custom build.

Data may be uploaded as a CSV or queried through connected data sources. The listed source categories include Shopify, Stripe, WooCommerce, eBay, Google Analytics 4, and Google Search Console. The service is intended for business and statistical questions about data, rather than open-ended assistant tasks.

Setup and configuration

Create an account at mcpanalytics.ai, then copy an API key from the account settings. Configure an MCP client with the endpoint and send the key in the X-API-Key header:

config.json
{
  "mcpServers": {
    "mcpanalytics": {
      "url": "https://api.mcpanalytics.ai/mcp/api-key",
      "headers": { "X-API-Key": "mcp_your_key_here" }
    }
  }
}

OAuth 2.0 through Auth0 is also available from compatible AI clients. Claude Code can be configured with the HTTP transport using its claude mcp add command and the same endpoint and header.

The endpoint supports discovery without a key: clients can connect and list tools, but they cannot execute operations. A direct tools/list JSON-RPC request can be sent with curl to inspect the available interface.

Tools and capabilities

The exposed tools cover the full analysis lifecycle and related account operations:

  • datasets_upload and datasets_list manage uploaded datasets.
  • connectors_list and connectors_query inspect and query connected sources.
  • create_analysis, modify_analysis, run_analysis, and build_status create, adjust, execute, and inspect analysis jobs.
  • reports_view, reports_list, and report_cards retrieve reports and report summaries.
  • ask_library asks questions across the saved report library.
  • discover_tools and tools_schema expose tool discovery and schemas.
  • agent_advisor supports the analysis-routing workflow.
  • account_link, billing, and about provide account and service information.

Example questions include comparing revenue between months, identifying customers likely to churn, and forecasting the next quarter from an uploaded file.

Limitations and notes

The service requires an account and API key, or an available Auth0 OAuth flow, to run operations. Discovery is available without authentication, but execution is not. Uploaded data is used to answer questions; retention details are governed by the service’s privacy policy. Reports name their methods and executed code, but the server is intended for data analysis rather than general-purpose assistance.

Reviews

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Frequently Asked Questions about MCP Analytics

embeddedlayers/mcp-analytics is an MCP server that connects AI clients to uploaded CSV data and business sources such as Shopify, Stripe, WooCommerce, eBay, GA4, and Search Console. Its main tools upload datasets, query connectors, run analyses, and retrieve reports with statistical methods, charts, tables, and interpretations.

Configure an MCP client with the Streamable HTTP endpoint and an X-API-Key header. Create an account at mcpanalytics.ai, copy the key from account settings, and use https://api.mcpanalytics.ai/mcp/api-key as the server URL. Auth0 OAuth 2.0 is also available through compatible clients.

Yes, embeddedlayers/mcp-analytics can be used from Cursor. Add its Streamable HTTP endpoint to the client configuration and provide the X-API-Key header, or use the available Auth0 OAuth 2.0 flow.

Yes, it can analyze an uploaded CSV file. Use the dataset upload capability, then ask a business or statistical question such as comparing revenue, forecasting sales, or identifying likely churn. The resulting report can include charts, tables, interpretation, method details, and executed code.

Yes, you can connect without a key and list the available tools in discovery mode. Running operations still requires authentication, so an API key or supported Auth0 OAuth 2.0 flow is needed for uploads, queries, analyses, and reports.

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