What is the key difference between DecisionNode and Agentram Mcp?

DecisionNode belongs to the Knowledge & Memory category with cloud HTTP/SSE execution, while Agentram Mcp belongs to the Knowledge & Memory category with local stdio execution.

How do I install DecisionNode or Agentram 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 DecisionNode and Agentram Mcp free or paid?

DecisionNode operates under a Free / Open Source model (No auth required). Agentram Mcp operates under a Free / Open Source model (No auth required).

Which MCP server has more tools and community activity?

DecisionNode lists 6 tools with 0 views and 81 GitHub stars. Agentram Mcp lists 6 tools with 0 views and 0 GitHub stars.

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

DecisionNode is listed under a Free / Open Source model with No auth required . Agentram Mcp 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 DecisionNode and Agentram Mcp at the same time in my AI client?

Yes! MCP clients support multi-server orchestration. You can include both decisionnode-decisionnode and seanmarkwei-agentram-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, DecisionNode has a Quality Score of 51 /100 ( Good ) with 0 views, 0 installs, and 81 GitHub stars. Agentram Mcp holds a Quality Score of 51 /100 ( Good ) with 0 views, 0 installs, and 0 GitHub stars.

Side-by-Side Model Context Protocol Comparison

DecisionNode vs Agentram Mcp

In-depth architectural comparison of the DecisionNode and Agentram 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

DecisionNode

Knowledge & Memory · Remote HTTP/SSE

Quality: 51/100 (Good) | Auth: No auth required

Agentram Mcp

Knowledge & Memory · Local stdio

Quality: 51/100 (Good) | Auth: No auth required

Verdict Summary: Choose DecisionNode if you need specialized Knowledge & Memory tools running via a hosted cloud SSE transport. Choose Agentram 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 DecisionNode when:

You need dedicated capabilities in the Knowledge & Memory domain.
You prefer remote streaming HTTP/SSE transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
Primary tools included: Store decisions as JSON with fields like id, scope, status, rationale, and constraints, Embed decisions using Gemini embedding model for semantic search, CLI commands for adding, searching, editing, deprecating, and exporting decisions.

Explore DecisionNode Details

Choose Agentram 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).
Primary tools included: Personal memory storage with agent ID and key, Shared namespaces for multi-agent memory collaboration, Text-based search across keys and values without embeddings.

Feature & Specification Comparison

Specification	DecisionNode decisionnode Knowledge & Memory	Agentram Mcp seanmarkwei Knowledge & Memory
Summary	Record development decisions as structured JSON, embed as vectors via Gemini, and search semantically over MCP. Shared store across Claude Code, Cursor, Windsurf, and any MCP client. CLI + MCP server, local-only, free Gemini embedding tier.	Persistent memory for AI agents through a simple key-value HTTP API. No vector database or embeddings required. Store, retrieve, search, and share memory across agents with shared namespaces and TTL support. npx -y agentram-mcp
Category & Scope

Tools & Capabilities Breakdown

DecisionNode Tools (6)

Store decisions as JSON with fields like id, scope, status, rationale, and constraints

Embed decisions using Gemini embedding model for semantic search

CLI commands for adding, searching, editing, deprecating, and exporting decisions

MCP server interface exposing add, search, update, delete, list, and history actions

Local web UI showing graph, vector space, and list views of decisions

Conflict detection on similar decisions and full audit trail with source tracking

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).

DecisionNode Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "decisionnode-decisionnode": {
      "url": "https://opensource.org/licenses/MIT"
    }
  }
}

Agentram Mcp Configuration

mcpServers (Claude Desktop / Cursor)

{
  "mcpServers": {
    "seanmarkwei-agentram-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/inspector"
      ]
    }
  }
}

Frequently Asked Questions

DecisionNode is categorized under Knowledge & Memory and uses a remote streaming HTTP/SSE transport. In contrast, Agentram Mcp belongs to Knowledge & Memory using local stdio subprocess. Select DecisionNode when you need capabilities focused on knowledge & memory and Agentram Mcp when you require tools for knowledge & memory.

More alternatives to DecisionNode More alternatives to Agentram Mcp Knowledge & Memory category hub