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Memory and knowledge · MCP server

Vulcanus: MCP server, setup and security

Vulcanus is an open-source MCP server published on GitHub that lets an AI agent retrieve project memory and detect summaries outdated by more recent decisions. Its repository has 5 stars and its last commit is from October 19, 2026 and it is licensed under MIT.

Actively maintained

What Vulcanus does

According to its repository: “Generates a Git-versioned Markdown vault as project memory, then serves it over MCP. recall returns a project's capsule plus the read-next list and warns when the capsule is older than the decisions beneath it; search ranks by layer; writes are scoped — update_capsule replaces one named section rath”

The Model Context Protocol (MCP) is a standard plug between an AI agent and an outside service: once the server is connected, its tools show up in the agent, which decides when to call them. Vulcanus is listed under Memory and knowledge, a category of 250 servers in the directory, where it ranks #237 by stars.

Topics declared by the repository: ai-agents, ai-memory, cli, knowledge-management, markdown, obsidian.

Installing and connecting Vulcanus

Distribution modes declared in the official MCP registry:

  • npm package (Node.js, usually started with npx)

It works with any MCP-compatible client (Claude Desktop, Claude Code, Cursor, VS Code, Codex and most recent agents). You add one entry to the client's MCP configuration: server name, start command or URL, environment variables. Project language: TypeScript. Listed systems: macOS, Windows, Linux.

Security: what to check

Connecting an MCP server means installing software that talks straight to your agent: it runs with your rights and the model trusts its tool descriptions. The risk therefore depends on what it can touch and how far its code can be trusted.

None of the risk signals we track (system access, secrets, missing licence, abandonment) applies to Vulcanus. 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 July 23, 2026, the sunsatosolutions/Vulcanus repository has 5 stars and 0 forks, with 0 open issues. It has more stars than 13% of the servers in the directory. It received changes this month. Version published in the registry: 0.6.3 (October 14, 2026).

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

Context7Context7 : 63.6K63.6KHindsightHindsight : 53.4K53.4KCogneeCognee : 32.7K32.7KSkill SeekersSkill Seekers : 15.2K15.2KHonchoHoncho : 7.8K7.8KBasic memoryBasic memory : 4.3K4.3KNoteDiscoveryNoteDiscovery : 2.8K2.8KMemantoMemanto : 2.3K2.3KVulcanusVulcanus : 55
GitHub stars of the most followed servers in the category (log scale); Vulcanus in orange. Checked October 25, 2026.

Verdict

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

Strengths

  • Active project: recent commit
  • Listed in the official MCP registry
  • Permissive licence (MIT)

Limitations

  • Few visible users

Frequently asked questions

How do I install Vulcanus?

Vulcanus is distributed as npm package (Node.js, usually started with npx). Add it to your client's MCP configuration (Claude Desktop, Cursor, VS Code…) following the README of sunsatosolutions/Vulcanus.

Is Vulcanus free?

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

Is Vulcanus 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 sunsatosolutions on GitHub. Check who maintains it before giving it access.

What are the alternatives to Vulcanus?

In the same category: Context7, Basic memory, Hindsight, and Cognee. Context7 and Basic memory 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