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MCP Analytics: MCP server, setup and security

MCP Analytics is an open-source MCP server that lets an AI agent analyze CSV or live service data and generate interactive HTML reports. Its repository has 8 stars and its last commit is from October 24, 2026 and it is licensed under MIT.

Actively maintainedRemote server

What MCP Analytics does

According to its repository: “Statistical analysis, forecasting and ML on CSV files or live data from Shopify, Stripe, GA4 and more, with interactive HTML reports.”

An MCP server gives an AI agent extra tools it calls during the conversation: the agent reads their description, decides to use them and gets the result back. MCP Analytics is listed under Data platforms, a category of 39 servers in the directory, where it ranks #26 by stars.

Topics declared by the repository: analytics, claude, cursor, data-science, machine-learning, statistics.

Installing and connecting MCP Analytics

Distribution modes declared in the official MCP registry:

  • remote server (streamable-http): you connect to it by URL, with nothing to install locally

Like any MCP server, you declare it in your client's configuration (Claude Desktop, Claude Code, Cursor, VS Code, Codex…): a name, the command that starts it or its URL, and any environment variables. The client then starts it and lists its tools. Project language: Python.

Security: what to check

An MCP server runs code with the rights you give it, and the model reads its tool descriptions: a malicious or compromised server can hijack the agent. Read the code, or pick a maintained and widely used server, before connecting it.

None of the risk signals we track (system access, secrets, missing licence, abandonment) applies to MCP Analytics. That does not replace reading the code.

Good practice: grant only the rights needed (least privilege), require human approval for irreversible actions, and run the AI agent security check.

Project activity and maintenance

Created on September 17, 2025, the embeddedlayers/mcp-analytics repository has 8 stars and 1 forks, with 4 open issues. It has more stars than 22% of the servers in the directory. It received changes this month. Version published in the registry: 1.0.8 (October 15, 2026).

Stars measure attention, not quality: a recent, heavily promoted project can collect them fast, while a useful but niche server will have few.

MCP server chartMCP server chart : 4.4K4.4KGitLab MCP Server (zereight)GitLab MCP Server (zereight) : 2.1K2.1KMCP mermaidMCP mermaid : 646646Dbt MCPDbt MCP : 612612WalkerOS FlowWalkerOS Flow : 347347QuackbackQuackback : 329329Search Console MCPSearch Console MCP : 308308MCP echartsMCP echarts : 270270MCP AnalyticsMCP Analytics : 88
GitHub stars of the most followed servers in the category (log scale); MCP Analytics in orange. Checked October 25, 2026.

Verdict

MCP Analytics is a reasonable choice; check the points below before connecting it.

Strengths

  • Active project: recent commit
  • Listed in the official MCP registry
  • Usable remotely, without installation
  • Permissive licence (MIT)

Limitations

  • Few visible users

Frequently asked questions

How do I install MCP Analytics?

MCP Analytics is distributed as a remote server. Add it to your client's MCP configuration (Claude Desktop, Cursor, VS Code…) following the README of embeddedlayers/mcp-analytics.

Is MCP Analytics free?

The code is published under the MIT licence and can be used free of charge.

Is MCP Analytics safe?

No risk signal found (system access, secrets, licence, abandonment). As with any MCP server, keep rights minimal and read the code before sensitive use.

Is it an official server?

It is published in the MCP registry under the verified namespace ai.mcpanalytics, which shows the publisher controls that domain.

What are the alternatives to MCP Analytics?

In the same category: MCP server chart, Dbt MCP, GitLab MCP Server (zereight), and WalkerOS Flow. MCP server chart and Dbt MCP score higher on our grid.

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Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score