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Graforest MCP Server

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

License Python PyPI

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:

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

The AI does the thinking. Graforest stores the results.


Cloud Deployment (LogicBlok Module)

Graforest MCP deploys as a LogicBlok module through the RationalBloks platform. No kubectl, Docker CLI, or cluster access needed.

Deploy via RationalBloks UI

  1. Log in at infra.rationalbloks.com
  2. Select the Graforest project → Modules → Deploy Module
  3. Settings:
    • Name: graforest-mcp
    • Type: logicblok
    • Repo: https://github.com/graforest/graforest-mcp
    • Dockerfile: Dockerfile (root of repo)
  4. Set environment variables:
    • GRAFOREST_RB_API_KEY — Graforest service account key (rb_sk_...)
    • RATIONALBLOKS_MCP_URL — https://logicblok.rationalbloks.com
    • TRANSPORT — http
    • HOST — 0.0.0.0
  5. Deploy. The platform handles: clone → build → push → K8s → TLS.

What the Platform Creates

Resource Value
Namespace customer-{project_code}-staging
Domain {module_code}-mod.customersblok.rationalbloks.com
Port 8000 with /health probes
TLS Auto-provisioned by cert-manager

Dockerfile

The included Dockerfile meets the LogicBlok module contract:

  • Port 8000
  • /health endpoint
  • Non-root user (UID 1000)
  • Multi-stage build with UV dependency caching

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

Release files for graforest-mcp 0.1.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for graforest-mcp 0.1.1
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Built distribution (wheel)

Table of built distributions (wheels) for graforest-mcp 0.1.1
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graforest_mcp-0.1.1-py3-none-any.whl Python 3 none any Details

Total release size: 52.5 kB

Release files / graforest_mcp-0.1.1.tar.gz

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