Tinybird MCP Server

Tinybird MCP Server – Connecting AI Assistants to Real‑Time Analytics

The Tinybird Model Context Protocol (MCP) server bridges the gap between AI assistants and live analytical data streams. It allows Claude, Gemini, or other MCP‑compliant agents to issue SQL queries and receive results directly from Tinybird’s high‑performance data lake, turning raw analytics into actionable insights within a conversational context. This solves the common pain point of developers needing to write custom connectors or API wrappers for each analytics platform; Tinybird’s MCP server standardises the interface, letting AI assistants query data as naturally as they would a database.

At its core, the Tinybird MCP server exposes a single resource type: “tinybird”. Each resource is defined by a query string and optional parameters such as the dataset name, authentication token, or custom headers. When an AI client requests a tool that references this resource, the server executes the query against Tinybird’s engine and streams back a structured JSON response. The simplicity of this model means developers can add new analytical capabilities by merely updating the query string, without touching code that handles authentication or pagination.

Key capabilities include:

• Real‑time analytics – Tinybird’s columnar engine processes millions of rows per second, enabling AI assistants to answer up‑to‑date business questions on the fly.
• Schema‑driven responses – The server automatically maps Tinybird columns to JSON fields, preserving type information for downstream tooling.
• Secure access – Authentication is handled via bearer tokens or API keys passed in the resource definition, ensuring that only authorized queries run.
• Extensibility – Developers can chain multiple Tinybird queries as separate tools, or combine them with other MCP resources (e.g., prompts or sampling) to build complex workflows.

Typical use cases include:

• Business intelligence chatbots that answer sales, marketing, or product metrics directly from the data lake.
• Operational dashboards where an AI assistant can surface alerts or trend analyses without manual report generation.
• Data‑driven decision support in customer service, where the assistant pulls historical interaction metrics to recommend next steps.
• Automated reporting that generates periodic summaries by querying Tinybird and formatting the output with a prompt.

Integrating Tinybird MCP into an AI workflow is straightforward: developers add a “tinybird” resource to their server configuration, expose it as a tool in the MCP client, and reference it within prompts or conversational logic. The AI assistant then calls the tool whenever a data‑centric question arises, receives the JSON payload instantly, and can embed it into responses or trigger downstream actions.

What sets Tinybird apart is its combination of speed, scalability, and SQL familiarity. Developers can write familiar queries, trust the engine to handle petabyte‑scale data, and rely on MCP’s standardized interface to keep AI assistants tightly coupled with analytics. This eliminates the need for bespoke data pipelines, reduces latency in information retrieval, and empowers AI applications to deliver precise, real‑time insights across any domain.