
ToolFront MCP Server
Securely connects AI agents to multiple databases simultaneously while enabling collaborative learning from team query patterns, all while keeping data private by running locally.
README
<br> <div align="center"> <img alt="toolfront" src="https://github.com/kruskal-labs/toolfront/blob/main/img/logo/toolfront_logo_light.png#gh-light-mode-only" width="80%"> <img alt="toolfront" src="https://github.com/kruskal-labs/toolfront/blob/main/img/logo/toolfront_logo_dark.png#gh-dark-mode-only" width="80%">
</div>
<br> <br>
AI agents lack context about your databases, while teams keep rewriting the same queries because past work often gets lost. ToolFront connects agents to your databases and feeds them your team's proven query patterns, so both agents and teammates can learn from each other and ship faster.
Features
- ⚡ One-step setup: Connect coding agents like Cursor, GitHub Copilot, and Claude to all your databases with a single command.
- 🔒 Privacy-first: Your data never leaves your machine, and is only shared between agents and databases through a secure MCP server.
- 🧠 Collaborative learning: The more your team uses ToolFront, the better your AI agents understand your databases and query patterns.
Quickstart
ToolFront runs on your computer through an MCP server, a secure protocol that lets apps provide context to LLM models.
Prerequisites
You'll need uv or Docker to run the MCP server, and optionally an API key to activate collaborative learning.
Running with UV:
<br>
Add this to your MCP config file to connect coding agents to ToolFront with UV:
{
# Rest of config file
"toolfront": {
"command": "uvx",
"args": [
"toolfront",
"DATABASE-URL-1",
"DATABASE-URL-2",
# Add other database URLs here
"--api-key", "YOUR-API-KEY" // Optional
]
}
}
Alternatively, run uvx toolfront
to download and start the ToolFront MCP server:
uvx toolfront "DATABASE-URL-1" "DATABASE-URL-2" [...] --api-key "YOUR-API-KEY"
[!TIP] Version control: Use
toolfront
to get the latest version automatically, or pin to a specific version e.g.toolfront==0.1.0
. This applies to both direct commands and MCP configuration.
Running with Docker
<br>
Add this to your MCP config file to connect coding agents to ToolFront with Docker:
{
# Rest of config file
"toolfront": {
"command": "docker",
"args": [
"run",
"-i",
"antidmg/toolfront",
"DATABASE-URL-1",
"DATABASE-URL-2",
# Add other database URLs here
"--api-key", "YOUR-API-KEY" // Optional
]
}
}
[!TIP] Localhost databases: When connecting to databases on localhost (like
postgresql://user:pass@localhost:5432/db
), add--network host
before the image name. Remote databases (cloud, external servers) work without this flag.
Alternatively, run the following command to download, pull, and run the ToolFront MCP container:
docker run -i antidmg/toolfront "DATABASE-URL-1" "DATABASE-URL-2" [...] --api-key "YOUR-API-KEY"
Collaborative In-context Learning
Data teams keep rewriting the same queries because past work often gets siloed, scattered, or lost. ToolFront teaches AI agents how your team works with your databases through in-context learning. With ToolFront, your agents can:
- Reason about historical query patterns
- Remember relevant tables and schemas
- Reference your and your teammates' work
[!NOTE] In-context learning is currently in open beta. To request an API key, please email Esteban at esteban@kruskal.ai.
Model Context Protocol (MCP)
ToolFront's MCP server comes with seven database tools for AI agents.
Databases
When configuring ToolFront, use the fully-specified connection URL for your databases:
Database | URL Example |
---|---|
BigQuery | bigquery://project/dataset |
DuckDB | duckdb:///path/to/db.duckdb |
MySQL | mysql://user:pass@host:port/db |
PostgreSQL | postgresql://user:pass@host:port/db |
Snowflake | snowflake://user:pass@account/db |
SQLite | sqlite:///path/to/db.sqlite |
Don't see your database? Submit an issue or pull request, or let us know in our Discord!
Tools
ToolFront provides AI agents with the following database tools:
Tool | Description |
---|---|
test |
Tests whether a data source connection is working |
discover |
Discovers and lists all configured databases and file sources |
scan |
Searches for tables using regex, fuzzy matching, or TF-IDF similarity |
inspect |
Inspects table schemas, showing column names, data types, and constraints |
sample |
Retrieves sample rows from tables to understand data content and format |
query |
Executes read-only SQL queries against databases with error handling |
learn |
Retrieves relevant queries or tables for in-context learning |
FAQ
<details> <summary><strong>How is ToolFront different from other database MCPs?</strong></summary> <br>
ToolFront has three key advantages: multi-database support, privacy-first architecture, and collaborative learning.
Multi-database support: While some general-purpose MCP servers happen to support multiple databases, most database MCPs only work with one database at a time, forcing you to manage separate MCP servers for each connection. ToolFront connects to all your databases in one place.
Privacy-first architecture: Other multi-database solutions route your data through the cloud, which racks up egress fees and creates serious privacy, security, and access control issues. ToolFront keeps everything local.
Collaborative learning: Database MCPs just expose raw database operations. ToolFront goes further by teaching your AI agents successful query patterns from your team's work, helping them learn your specific schemas and data relationships to improve over time.
</details>
<details> <summary><strong>How is collaborative learning different from agent memory?</strong></summary> <br>
Agent memory stores conversation histories for individuals, whereas ToolFront's collaborative learning remembers relational query patterns across your team and databases.
When one teammate queries a database, that knowledge becomes available to other team members using ToolFront. The system gets smarter over time by learning from your team's collective database interactions.
</details>
<details> <summary><strong>What data is collected during collaborative learning?</strong></summary> <br>
With an API key, ToolFront only logs the query syntax and their descriptions generated by your AI agents. It never collects your actual database content or personal information. For details, see the query
and learn
functions in tools.py.
</details>
<details> <summary><strong>How does ToolFront keep my data safe?</strong></summary> <br>
- Local execution: All database connections and queries run on your machine
- No secrets exposure: Database credentials are never shared with AI agents
- Read-only operations: Only safe, read-only database queries are allowed
- No data transmission: Your database content never leaves your environment
- Secure MCP protocol: Direct communication between agents and databases with no third-party storage
</details>
<details> <summary><strong>How do I troubleshoot connection issues?</strong></summary> <br>
Run the uvx toolfront
or docker run
commands with your database URLs directly from the command line. ToolFront automatically tests all connections before starting and shows detailed error messages if any connection fails.
If you're still having trouble, double-check your database URLs using the examples in the Databases section above.
</details>
Support & Community
Need help with ToolFront? We're here to assist:
- Discord: Join our community server for real-time help and discussions
- Issues: Report bugs or request features on GitHub Issues
Contributing
See CONTRIBUTING.md for guidelines on how to contribute to ToolFront.
License
ToolFront is released under the GPL License v3. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the GPL v3 License. For the full license text, see the LICENSE file in the repository.
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。