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Monitoring and observability · MCP server

Marmot Data Catalog: MCP server, setup and security

Marmot Data Catalog is an open-source MCP server that lets an AI agent search data assets, explore their lineage and identify ownership. Its repository has 623 stars and its last commit is from October 24, 2026 and it is licensed under MIT.

Score
73/100
Adoption (stars) 28/40
Maintenance 30/30
Framework (licence, registry, official) 15/30
Actively maintainedRemote server

What Marmot Data Catalog does

According to its repository: “Open-source data catalog. Search assets, explore lineage, and find ownership.”

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. Marmot Data Catalog is listed under Monitoring and observability, a category of 57 servers in the directory, where it ranks #5 by stars.

Topics declared by the repository: bigdata, data-catalog, data-collaboration, data-discovery, data-exploration, data-governance.

Installing and connecting Marmot Data Catalog

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: Go.

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 Marmot Data Catalog. 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 March 12, 2025, the marmotdata/marmot repository has 623 stars and 31 forks, with 21 open issues. It has more stars than 89% of the servers in the directory. It received changes this month. Version published in the registry: 1.0.0 (March 21, 2026).

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

NetdataNetdata : 81.1K81.1KOpenstatusOpenstatus : 9.2K9.2KOneUptimeOneUptime : 7.8K7.8KMCP grafanaMCP grafana : 3.6K3.6KMarmot Data CatalogMarmot Data Catalog : 623623Watch skillWatch skill : 520520HomebutlerHomebutler : 305305Zabbix MCP serverZabbix MCP server : 261261
GitHub stars of the most followed servers in the category (log scale); Marmot Data Catalog in orange. Checked October 25, 2026.

Verdict

Marmot Data Catalog is a reasonable choice; check the points below before connecting it.

Strengths

  • Active project: recent commit
  • Widely followed: 623 stars
  • Listed in the official MCP registry
  • Usable remotely, without installation
  • Permissive licence (MIT)

Frequently asked questions

How do I install Marmot Data Catalog?

Marmot Data Catalog is distributed as a remote server. Add it to your client's MCP configuration (Claude Desktop, Cursor, VS Code…) following the README of marmotdata/marmot.

Is Marmot Data Catalog free?

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

Is Marmot Data Catalog 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?

Nothing says so: it is a project published by marmotdata on GitHub. Check who maintains it before giving it access.

What are the alternatives to Marmot Data Catalog?

In the same category: Netdata, MCP grafana, Openstatus, and OneUptime. Netdata and MCP grafana 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