Graforest MCP
Enables AI agents to build, populate, and search knowledge graphs by providing tools for entity extraction, relationship mapping, and graph traversal. It manages the underlying database infrastructure so users can create searchable knowledge bases from text through natural language commands.
README
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.
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
- Log in at infra.rationalbloks.com
- Select the Graforest project → Modules → Deploy Module
- Settings:
- Name:
graforest-mcp - Type:
logicblok - Repo:
https://github.com/graforest/graforest-mcp - Dockerfile:
Dockerfile(root of repo)
- Name:
- Set environment variables:
GRAFOREST_RB_API_KEY— Graforest service account key (rb_sk_...)RATIONALBLOKS_MCP_URL—https://logicblok.rationalbloks.comTRANSPORT—httpHOST—0.0.0.0
- 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
/healthendpoint- 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 guidegraforest://docs/knowledge-graph— Knowledge graph concepts
Prompts:
ingest-content— Guided content ingestion workflowexplore-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
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。