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MCP server · AI

LLM Toolshed MCP Server

MCP Server for LLM Model Agent Toolshed with RAG and CAG capabilities for BambiSleep knowledge base

Unclaimed last commit 10 months ago ai
43Fair

Scored 3 months ago · breakdown

About LLM Toolshed MCP Server

LLM Toolshed MCP Server is an MCP server published by HarleyVader in the AI category: mCP Server for LLM Model Agent Toolshed with RAG and CAG capabilities for BambiSleep knowledge base. It has been installed 0 times through Conduid.

Six months or more without a commit doesn't mean the server is broken, but check the open issues (0) before depending on it in production.

Install

Install
npx llm-toolshed-mcp-server

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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Security checks

  • ·README presentNot checked yet.
  • ·License declaredNot checked yet.
  • ·Tests presentNot checked yet.
  • ·Dependencies pinnedNot checked yet.
  • ·No dynamic code executionNot checked yet.
  • !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.

README

LLM Toolshed MCP Server

An MCP (Model Context Protocol) server providing RAG (Retrieval Augmented Generation) and CAG (Context Augmented Generation) capabilities for BambiSleep knowledge base.

Features

Tools

  • rag_query: Perform RAG queries on BambiSleep data
  • cag_context: Build context-augmented generation from knowledge graph
  • extract_entities: Extract entities and relationships for knowledge graph
  • semantic_search: Semantic search across content
  • get_metadata: Get knowledge base metadata

Resources

  • bambisleep://data/structured - Full structured JSON data
  • bambisleep://data/faq - FAQ content
  • bambisleep://data/sessions - Session index
  • bambisleep://data/triggers - Triggers documentation
  • bambisleep://data/safety - Safety information

Installation

cd llm-toolshed-mcp-server
npm install

Usage

Run the server

npm start

Configure in Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "llm-toolshed": {
      "command": "node",
      "args": ["/home/melkanea/llm-toolshed-mcp-server/index.js"]
    }
  }
}

Data Structure

The server works with structured JSON data from bambisleep.info:

{
  "metadata": {
    "source": "bambisleep.info",
    "fetched_at": "timestamp",
    "type": "bambi_sleep_wiki_content",
    "version": "1.0"
  },
  "content": {
    "faq": {...},
    "sessions": {...},
    "triggers": {...},
    "safety": {...},
    "transcripts": {...}
  },
  "rag_vectors": {...},
  "cag_context": {...}
}

Example Queries

RAG Query

{
  "name": "rag_query",
  "arguments": {
    "query": "what is bambi sleep",
    "section": "faq",
    "max_results": 5
  }
}

CAG Context

{
  "name": "cag_context",
  "arguments": {
    "entity": "Bambi",
    "depth": 2
  }
}

Extract Entities

{
  "name": "extract_entities",
  "arguments": {
    "section": "triggers"
  }
}

Architecture

  • MCP SDK: Uses @modelcontextprotocol/sdk for server implementation
  • Resources: Exposes BambiSleep data as MCP resources
  • Tools: Provides RAG/CAG tools for LLM agents
  • Transport: Stdio-based communication

License

MIT

README mirrored from the source repository 3 months ago. The original is authoritative.

Questions

About LLM Toolshed MCP Server

How do I install LLM Toolshed MCP Server?

Run npx llm-toolshed-mcp-server, then add the server to your MCP client's configuration. Conduid has recorded 0 installs, so the command is known to work with current clients.

Is LLM Toolshed MCP Server safe to use with an AI agent?

Its trust score is 43 out of 100 (fair). It passes 0 of 1 static security checks; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is LLM Toolshed MCP Server still maintained?

The last commit was 10 months ago, with 0 open issues. That's long enough that you should check whether the maintainer is responding to issues before depending on it.