Tinybird Mcp 服务器
概览
什么是 MCP-Tinybird?
MCP-Tinybird 是一个开源项目,托管在 GitHub 上,隶属于 Tinybirdco 组织。它为开发者提供了一个强大的工具,旨在高效构建和管理数据管道。该代码库旨在促进 Tinybird 功能与各种应用程序的集成,使用户能够利用实时数据处理和分析。
MCP-Tinybird 的特点
- 实时数据处理:MCP-Tinybird 使用户能够实时处理和分析数据,非常适合需要即时洞察的应用程序。
- 用户友好的界面:该项目提供了一个简单明了的界面,简化了数据管道的管理,使各个技能水平的开发者都能轻松使用。
- 开源:作为一个开源项目,MCP-Tinybird 鼓励社区贡献和协作,允许开发者增强其功能和特性。
- 集成能力:该代码库支持与各种数据源和服务的集成,提供了数据摄取和处理的灵活性。
- 文档和支持:提供全面的文档,以帮助用户入门并解决可能遇到的任何问题。
如何使用 MCP-Tinybird
-
克隆代码库:首先使用以下命令从 GitHub 克隆 MCP-Tinybird 代码库到本地计算机:
git clone https://github.com/tinybirdco/mcp-tinybird.git
-
安装依赖:导航到项目目录并安装必要的依赖。这通常可以使用 npm 或 yarn 等包管理器完成:
cd mcp-tinybird npm install
-
配置您的环境:根据代码库中提供的文档设置您的环境变量和配置文件。
-
运行应用程序:启动应用程序以开始处理数据。通常可以使用以下命令完成:
npm start
-
探索和自定义:利用 MCP-Tinybird 的功能构建您的数据管道。您可以根据特定需求自定义应用程序,并与其他服务集成。
常见问题解答
Q1: MCP-Tinybird 是免费使用的吗?
A1: 是的,MCP-Tinybird 是一个开源项目,这意味着它可以在 Apache-2.0 许可证的条款下免费使用和修改。
Q2: 我可以为 MCP-Tinybird 项目做贡献吗?
A2: 当然可以!欢迎贡献。您可以分叉代码库,进行更改,并提交拉取请求以供审核。
Q3: 我在哪里可以找到 MCP-Tinybird 的文档?
A3: 文档通常在代码库本身中提供,通常在 README.md
文件或专门的 docs
文件夹中。
Q4: MCP-Tinybird 支持哪些技术?
A4: MCP-Tinybird 旨在与各种数据源协同工作,并可以与多种技术集成,增强其在不同用例中的多功能性。
Q5: 我该如何报告问题或错误?
A5: 您可以通过导航到 MCP-Tinybird GitHub 代码库中的“问题”选项卡,提交一个新问题,并详细描述问题来报告问题。
详情
Tinybird MCP server
An MCP server to interact with a Tinybird Workspace from any MCP client.
<a href="https://glama.ai/mcp/servers/53l5ojnx30"><img width="380" height="200" src="https://glama.ai/mcp/servers/53l5ojnx30/badge" alt="Tinybird server MCP server" /></a>
Features
- Query Tinybird Data Sources using the Tinybird Query API
- Get the result of existing Tinybird API Endpoints with HTTP requests
- Push Datafiles
It supports both SSE and STDIO modes.
Usage examples
Setup
Installation
Using MCP package managers
Smithery
To install Tinybird MCP for Claude Desktop automatically via Smithery:
npx @smithery/cli install @tinybirdco/mcp-tinybird --client claude
mcp-get
You can install the Tinybird MCP server using mcp-get:
npx @michaellatman/mcp-get@latest install mcp-tinybird
Prerequisites
MCP is still very new and evolving, we recommend following the MCP documentation to get the MCP basics up and running.
You'll need:
Configuration
1. Configure Claude Desktop
Create the following file depending on your OS:
On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
Paste this template in the file and replace <TINYBIRD_API_URL>
and <TINYBIRD_ADMIN_TOKEN>
with your Tinybird API URL and Admin Token:
{
"mcpServers": {
"mcp-tinybird": {
"command": "uvx",
"args": [
"mcp-tinybird",
"stdio"
],
"env": {
"TB_API_URL": "<TINYBIRD_API_URL>",
"TB_ADMIN_TOKEN": "<TINYBIRD_ADMIN_TOKEN>"
}
}
}
}
2. Restart Claude Desktop
SSE mode
Alternatively, you can run the MCP server in SSE mode by running the following command:
uvx mcp-tinybird sse
This mode is useful to integrate with an MCP client that supports SSE (like a web app).
Prompts
The server provides a single prompt:
- tinybird-default: Assumes you have loaded some data in Tinybird and want help exploring it.
- Requires a "topic" argument which defines the topic of the data you want to explore, for example, "Bluesky data" or "retail sales".
You can configure additional prompt workflows:
- Create a prompts Data Source in your workspace with this schema and append your prompts. The MCP loads
prompts
on initialization so you can configure it to your needs:
SCHEMA >
`name` String `json:$.name`,
`description` String `json:$.description`,
`timestamp` DateTime `json:$.timestamp`,
`arguments` Array(String) `json:$.arguments[:]`,
`prompt` String `json:$.prompt`
Tools
The server implements several tools to interact with the Tinybird Workspace:
list-data-sources
: Lists all Data Sources in the Tinybird Workspacelist-pipes
: Lists all Pipe Endpoints in the Tinybird Workspaceget-data-source
: Gets the information of a Data Source given its name, including the schema.get-pipe
: Gets the information of a Pipe Endpoint given its name, including its nodes and SQL transformation to understand what insights it provides.request-pipe-data
: Requests data from a Pipe Endpoints via an HTTP request. Pipe endpoints can have parameters to filter the analytical data.run-select-query
: Allows to run a select query over a Data Source to extract insights.append-insight
: Adds a new business insight to the memo resourcellms-tinybird-docs
: Contains the whole Tinybird product documentation, so you can use it to get context about what Tinybird is, what it does, API reference and more.save-event
: This allows to send an event to a Tinybird Data Source. Use it to save a user generated prompt to the prompts Data Source. The MCP server feeds from the prompts Data Source on initialization so the user can instruct the LLM the workflow to follow.analyze-pipe
: Uses the Tinybird analyze API to run a ClickHouse explain on the Pipe Endpoint query and check if indexes, sorting key, and partition key are being used and propose optimizations suggestionspush-datafile
: Creates a remote Data Source or Pipe in the Tinybird Workspace from a local datafile. Use the Filesystem MCP to save files generated by this MCP server.
Development
Config
If you are working locally add two environment variables to a .env
file in the root of the repository:
TB_API_URL=
TB_ADMIN_TOKEN=
For local development, update your Claude Desktop configuration:
{
"mcpServers": {
"mcp-tinybird_local": {
"command": "uv",
"args": [
"--directory",
"/path/to/your/mcp-tinybird",
"run",
"mcp-tinybird",
"stdio"
]
}
}
}
<details>
<summary>Published Servers Configuration</summary>
"mcpServers": {
"mcp-tinybird": {
"command": "uvx",
"args": [
"mcp-tinybird"
]
}
}
</details>
Building and Publishing
To prepare the package for distribution:
- Sync dependencies and update lockfile:
uv sync
- Build package distributions:
uv build
This will create source and wheel distributions in the dist/
directory.
- Publish to PyPI:
uv publish
Note: You'll need to set PyPI credentials via environment variables or command flags:
- Token:
--token
orUV_PUBLISH_TOKEN
- Or username/password:
--username
/UV_PUBLISH_USERNAME
and--password
/UV_PUBLISH_PASSWORD
Debugging
Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.
You can launch the MCP Inspector via npm
with this command:
npx @modelcontextprotocol/inspector uv --directory /Users/alrocar/gr/mcp-tinybird run mcp-tinybird
Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
Monitoring
To monitor the MCP server, you can use any compatible Prometheus client such as Grafana. Learn how to monitor your MCP server here.
Server配置
{
"mcpServers": {
"mcp-tinybird": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/metorial/mcp-container--tinybirdco--mcp-tinybird--mcp-tinybird",
"mcp-tinybird stdio"
],
"env": {
"TB_API_URL": "tb-api-url",
"TB_ADMIN_TOKEN": "tb-admin-token"
}
}
}
}