Graforest MCP Server

Build knowledge graphs with AI. 13 tools for creating, populating, searching, and exploring knowledge graphs through the Model Context Protocol.

What Is This?

Graforest MCP lets AI agents (Claude, Cursor, VS Code, etc.) build and query knowledge graphs. No database setup. No Neo4j config. Just tell your AI agent what you want to know.

"Create a knowledge graph about organic chemistry and populate it from my notes"
→ 2 minutes later: Searchable knowledge graph with entities and relationships

The AI agent handles intelligence (entity extraction, reasoning). Graforest handles data (storage, search, traversal).

Installation

pip install graforest-mcp

Quick Start

1. Get Your API Key

Visit graforest.ai/settings and create an API key (gf_sk_...).

2. Configure Your AI Agent

VS Code — Add to .vscode/mcp.json:

{
  "servers": {
    "graforest": {
      "command": "uvx",
      "args": ["graforest-mcp"],
      "env": {
        "GRAFOREST_API_KEY": "gf_sk_your_key_here"
      }
    }
  }
}

Cursor — Add to .cursor/mcp.json:

{
  "mcpServers": {
    "graforest": {
      "command": "uvx",
      "args": ["graforest-mcp"],
      "env": {
        "GRAFOREST_API_KEY": "gf_sk_your_key_here"
      }
    }
  }
}

Claude Desktop — Add to claude_desktop_config.json:

{
  "mcpServers": {
    "graforest": {
      "command": "uvx",
      "args": ["graforest-mcp"],
      "env": {
        "GRAFOREST_API_KEY": "gf_sk_your_key_here"
      }
    }
  }
}

Smithery:

npx @smithery/cli install @graforest/mcp

13 Tools

Provisioning (3 tools)

Tool	Description
`create_knowledge_project`	Provision a new knowledge graph (Neo4j)
`list_knowledge_projects`	List all graph projects
`delete_knowledge_project`	Delete a graph project permanently

Data Write (2 tools)

Tool	Description
`add_knowledge_nodes`	Bulk create entities (max 500/batch)
`add_knowledge_relationships`	Bulk create relationships (max 500/batch)

Data Read (6 tools)

Tool	Description
`search_knowledge_graph`	Full-text search across all node fields
`get_knowledge_schema`	Get entity types, relationship types, and fields
`get_knowledge_statistics`	Node and relationship counts by type
`traverse_knowledge_graph`	Walk connections from any node
`list_knowledge_entities`	List entities by type (paginated)
`get_knowledge_entity`	Get a single entity by ID

Ingestion (1 tool)

Tool	Description
`ingest_text_content`	Prepare text for the 3-call extraction workflow

Utility (1 tool)

Tool	Description
`fetch_url_content`	Scrape a URL and return clean text

3-Call Ingestion Workflow

The recommended way to populate a knowledge graph from text:

ingest_text_content(project_code, text) → Returns the graph schema + extraction instructions
LLM extracts all entities and relationships from the text (guided by the instructions)
add_knowledge_nodes + add_knowledge_relationships → Bulk write everything

The AI does the thinking. Graforest stores the results.

Architecture

AI Agent → graforest-mcp → Graph APIs (Neo4j databases)
                         → RationalBloks API (infrastructure provisioning)

No AI inside the MCP server — the LLM is the intelligence, Graforest is the data layer
Dual transport: STDIO (local IDEs) + HTTP/SSE (cloud deployment)
API key auth: gf_sk_ prefix for all Graforest keys

Resources & Prompts

Resources:

graforest://docs/getting-started — Quick start guide
graforest://docs/knowledge-graph — Knowledge graph concepts

Prompts:

ingest-content — Guided content ingestion workflow
explore-graph — Guided graph exploration workflow

Environment Variables

Variable	Required	Default	Description
`GRAFOREST_API_KEY`	Yes (STDIO)	—	Your Graforest API key
`TRANSPORT`	No	`stdio`	Transport mode: `stdio` or `http`
`PORT`	No	`8000`	HTTP server port
`HOST`	No	`0.0.0.0`	HTTP server bind address

Support

Website: graforest.ai
Documentation: graforest.ai/docs
Email: support@graforest.ai

Graforest MCP