What is the key difference between Dakera Mcp and Shelfmark?

Dakera Mcp belongs to the Knowledge & Memory category with local stdio execution, while Shelfmark belongs to the Knowledge & Memory category with local stdio execution.

How do I install Dakera Mcp or Shelfmark in Claude Desktop or Cursor?

Copy the mcpServers JSON configuration snippet provided on this page into your claude_desktop_config.json or ~/.cursor/mcp.json file and restart your AI client.

Are Dakera Mcp and Shelfmark free or paid?

Dakera Mcp operates under a Free / Open Source model (No auth required). Shelfmark operates under a Free / Open Source model (No auth required).

Which MCP server has more tools and community activity?

Dakera Mcp lists 0 tools with 0 views and 8 GitHub stars. Shelfmark lists 0 tools with 0 views and 0 GitHub stars.

Can I use both Dakera Mcp and Shelfmark together in the same MCP host?

Yes! MCP hosts like Claude Desktop, Cursor, Windsurf, Cline, and VS Code support configuring multiple servers under the mcpServers object simultaneously.

How do I install Dakera Mcp or Shelfmark in Claude Desktop, Cursor, or Windsurf?

Both servers follow the standard Model Context Protocol configuration format. Simply copy the JSON block from the configuration section above and paste it into your AI client's mcpServers configuration object (for instance in claude_desktop_config.json or ~/.cursor/mcp.json ), then completely restart or refresh the application.

Are Dakera Mcp and Shelfmark free to use, or do they require API keys?

Dakera Mcp is listed under a Free / Open Source model with No auth required . Shelfmark operates under a Free / Open Source model with No auth required . If environment variables are required (such as API keys), be sure to define them under the env key in your MCP client's configuration file.

Can I run both Dakera Mcp and Shelfmark at the same time in my AI client?

Yes! MCP clients support multi-server orchestration. You can include both dakera-mcp and shelfmark as distinct keys inside the single mcpServers object in your configuration file. Your AI assistant will automatically select and call the appropriate tool from either server during chat sessions.

Which MCP server has higher directory engagement and quality ratings?

On AllMCPs, Dakera Mcp has a Quality Score of 43 /100 ( Fair ) with 0 views, 0 installs, and 8 GitHub stars. Shelfmark holds a Quality Score of 33 /100 ( Emerging ) with 0 views, 0 installs, and 0 GitHub stars.

Side-by-Side Model Context Protocol Comparison

Dakera Mcp vs Shelfmark

In-depth architectural comparison of the Dakera Mcp and Shelfmark MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.

At a Glance & Executive Verdict

Dakera Mcp

Knowledge & Memory · Local stdio

Quality: 43/100 (Fair) | Auth: No auth required

Shelfmark

Knowledge & Memory · Local stdio

Quality: 33/100 (Emerging) | Auth: No auth required

Verdict Summary: Choose Dakera Mcp if you need specialized Knowledge & Memory tools running via a local process. Choose Shelfmark if your workspace requires Knowledge & Memory integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.

Which MCP Server Should You Choose?

Choose Dakera Mcp when:

You need dedicated capabilities in the Knowledge & Memory domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).

Explore Dakera Mcp Details

Choose Shelfmark when:

You need dedicated capabilities in the Knowledge & Memory domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).

Explore Shelfmark Details

Feature & Specification Comparison

Specification	D Dakera Mcp Knowledge & Memory	S Shelfmark Knowledge & Memory
Summary	Decay-weighted vector memory for AI agents — 83 MCP tools: store, recall, search, knowledge graphs.	Local, privacy-first document catalogue for AI agents: metadata-only discovery, no cloud, no RAG.
Category & Scope	Knowledge & Memory	Knowledge & Memory
Quality signal

Tools & Capabilities Breakdown

Dakera Mcp Tools (0)

No explicit tool names declared in metadata yet. Check project README on main listing page.

Shelfmark Tools (0)

No explicit tool names declared in metadata yet. Check project README on main listing page.

Ready-to-Paste Client Configurations

Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).

Dakera Mcp Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "dakera-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@dakera-ai/dakera-mcp"
      ]
    }
  }
}

Shelfmark Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "shelfmark": {
      "command": "npx",
      "args": [
        "-y",
        "shelfmark"
      ]
    }
  }
}

Frequently Asked Questions

Dakera Mcp is categorized under Knowledge & Memory and uses a local stdio subprocess. In contrast, Shelfmark belongs to Knowledge & Memory using local stdio subprocess. Select Dakera Mcp when you need capabilities focused on knowledge & memory and Shelfmark when you require tools for knowledge & memory.

More alternatives to Dakera Mcp More alternatives to Shelfmark Knowledge & Memory category hub

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