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https://github.com/tinybirdco/mcp-tinybird


https://github.com/tinybirdco/mcp-tinybird

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README 

        
# Tinybird MCP server



An MCP server to interact with a Tinybird Workspace from any MCP client.



## Features



- Query Tinybird Data Sources using the Tinybird Query API

- Get the result of existing Tinybird API Endpoints with HTTP requests

- Push Datafiles



## Usage examples



- [Bluesky metrics](https://bsky.app/profile/alasdairb.com/post/3lbx2mq5urk22) ([Claude transcript](https://www.tinybird.co/blog-posts/claude-analyze-bluesky-data-tinybird-mcp-server))

- [Web analytics starter kit metrics](https://github.com/tinybirdco/web-analytics-starter-kit) ([video](https://x.com/alrocar/status/1861849648882688341)]



## Setup



### Prerequisites



MCP is still very new and evolving, we recommend following the [MCP documentation](https://modelcontextprotocol.io/quickstart#prerequisites) to get the MCP basics up and running.



You'll need:

- [Tinybird Account & Workspace](https://www.tinybird.co/)

- [Claude Desktop](https://claude.ai/)

- [uv](https://docs.astral.sh/uv/getting-started/installation/)



### 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 `` and `` with your Tinybird API URL and Admin Token:



```json

{

    "mcpServers": {

        "mcp-tinybird": {

            "command": "uvx",

            "args": [

                "mcp-tinybird"

            ],

            "env": {

                "TB_API_URL": "",

                "TB_ADMIN_TOKEN": ""

            }

        }

    }

}

```



#### 2. Restart Claude Desktop



## Prompts



The server provides a single prompt:

- [tinybird-default](https://github.com/tinybirdco/mcp-tinybird/blob/93dd9e1d3c0e33f408fe88297151a44c1dfc049c/src/mcp-tinybird/server.py#L20): 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:

```bash

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 Workspace

- `list-pipes`: Lists all Pipe Endpoints in the Tinybird Workspace

- `get-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 resource

- `llms-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 suggestions

- `push-datafile`: Creates a remote Data Source or Pipe in the Tinybird Workspace from a local datafile. Use the [Filesystem MCP](https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem) 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:



```sh

TB_API_URL=

TB_ADMIN_TOKEN=

```



```json

{

  "mcpServers": {

    "mcp-tinybird_local": {

      "command": "uv",

      "args": [

        "--directory",

        "/Users/alrocar/gr/mcp-tinybird",

        "run",

        "mcp-tinybird"

      ]

    }

  }

}

```



### Building and Publishing



To prepare the package for distribution:



1. Sync dependencies and update lockfile:

```bash

uv sync

```



2. Build package distributions:

```bash

uv build

```



This will create source and wheel distributions in the `dist/` directory.



3. Publish to PyPI:

```bash

uv publish

```



Note: You'll need to set PyPI credentials via environment variables or command flags:

- Token: `--token` or `UV_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](https://github.com/modelcontextprotocol/inspector).



You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command:



```bash

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.