What is the key difference between Kage and Assistant Mcp?

Kage belongs to the Knowledge & Memory category with local stdio execution, while Assistant Mcp belongs to the Knowledge & Memory category with local stdio execution.

How do I install Kage or Assistant Mcp 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 Kage and Assistant Mcp free or paid?

Kage operates under a Free / Open Source model (No auth required). Assistant Mcp operates under a BYOK (Pay Provider Direct) model (API Key required).

Which MCP server has more tools and community activity?

Kage lists 6 tools with 0 views and 32 GitHub stars. Assistant Mcp lists 4 tools with 1 views and 45 GitHub stars.

Can I use both Kage and Assistant Mcp 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 Kage or Assistant Mcp 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 Kage and Assistant Mcp free to use, or do they require API keys?

Kage is listed under a Free / Open Source model with No auth required . Assistant Mcp operates under a BYOK (Pay Provider Direct) model with API Key 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 Kage and Assistant Mcp at the same time in my AI client?

Yes! MCP clients support multi-server orchestration. You can include both kage-core-kage and pinecone-io-assistant-mcp 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, Kage has a Quality Score of 55 /100 ( Good ) with 0 views, 0 installs, and 32 GitHub stars. Assistant Mcp holds a Quality Score of 41 /100 ( Fair ) with 1 views, 0 installs, and 45 GitHub stars.

Side-by-Side Model Context Protocol Comparison

Kage vs Assistant Mcp

In-depth architectural comparison of the Kage and Assistant Mcp 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

Kage

Knowledge & Memory · Local stdio

Quality: 55/100 (Good) | Auth: No auth required

Assistant Mcp

Knowledge & Memory · Local stdio

Quality: 41/100 (Fair) | Auth: API Key required

Verdict Summary: Choose Kage if you need specialized Knowledge & Memory tools running via a local process. Choose Assistant Mcp 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 Kage 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).
Primary tools included: Memory stored as plain JSON in git following Open Knowledge Format, Deterministic verification rejecting stale or hallucinated citations, Local-only operation with no account or API key required.

Explore Kage Details

Choose Assistant Mcp when:

You need dedicated capabilities in the Knowledge & Memory domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: API Key required (BYOK (Pay Provider Direct)).
You have access to required keys: PINECONE_API_KEY, PINECONE_ASSISTANT_HOST, LOG_LEVEL.
Primary tools included: Connects to Pinecone Assistant via API, Retrieves multiple results with configurable count, Supports environment variable configuration for API key and host.

Explore Assistant Mcp Details

Feature & Specification Comparison

Specification	Kage kage-core Knowledge & Memory	Assistant Mcp pinecone-io Knowledge & Memory
Summary	Verified, git-native memory for coding agents. Memory is plain JSON packets committed in your repo, each checked against the code it cites — hallucinated citations rejected at write, stale or changed memory withheld at recall, plus diff-time stale-catch. Local-only (BM25 + vectors), no account, no API key. npx -y @kage-core/kage-graph-mcp install	Connects to your Pinecone Assistant and gives the agent context from its knowledge engine.
Category & Scope	Knowledge & Memory	Knowledge & Memory
Quality signal	55/100 (Good)	41/100 (Fair)
Transport Protocol	Local Subprocess (stdio)	Local Subprocess (stdio)
Auth Requirement	No auth required	API Key required
Pricing Model	Free / Open Source	BYOK (Pay Provider Direct)
Required Env Vars	None required	`PINECONE_API_KEYPINECONE_ASSISTANT_HOSTLOG_LEVEL`
Compatible Clients	Claude DesktopCursorWindsurfClineVS Code	Claude DesktopCursorWindsurfClineVS Code
Install path signal	`npx · high`	`npx · low`
Engagement & Health	0 views 0 copies 0 upvotes 32 stars	1 views 0 copies 0 upvotes 45 stars
Verified / Official	Community Listing	Community Listing
Open full listing	View Kage Listing	View Assistant Mcp Listing

Tools & Capabilities Breakdown

Kage Tools (6)

Memory stored as plain JSON in git following Open Knowledge Format

Deterministic verification rejecting stale or hallucinated citations

Local-only operation with no account or API key required

Background proxy for zero-wiring integration with Anthropic agents

Per-directory auto-attach configuration for seamless agent routing

Compatibility with multiple coding agents including Claude Code and Codex

Assistant Mcp Tools (4)

Connects to Pinecone Assistant via API

Retrieves multiple results with configurable count

Supports environment variable configuration for API key and host

Can run as a Docker container or native Rust binary

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).

Kage Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "kage-core-kage": {
      "command": "npx",
      "args": [
        "-y",
        "@kage-core/kage-graph-mcp"
      ]
    }
  }
}

Assistant Mcp Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "pinecone-io-assistant-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "pinecone-io-assistant-mcp"
      ],
      "env": {
        "PINECONE_API_KEY": "YOUR_PINECONE_API_KEY_HERE",
        "PINECONE_ASSISTANT_HOST": "YOUR_PINECONE_ASSISTANT_HOST_HERE",
        "LOG_LEVEL": "YOUR_LOG_LEVEL_HERE"
      }
    }
  }
}

Frequently Asked Questions

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

More alternatives to Kage More alternatives to Assistant Mcp Knowledge & Memory category hub