> ## Documentation Index
> Fetch the complete documentation index at: https://tokenterminal.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> To query the Token Terminal data catalog, read https://tokenterminal.com/docs/catalog/agents-manual.md first. It is the whole catalog as one page: table naming grammar, key columns, partition and cluster rules, units, additivity, and the tables that are documented but not served yet.
> Never query a catalog table on a time bound alone. Also filter its cluster key, which you read from INFORMATION_SCHEMA.COLUMNS; an empty result means the object is a view, whose pruning contract is on its page. Compute is billed to the caller's own Google Cloud project.

# Introduction

> Token Terminal is a full stack onchain data platform focused on standardizing financial and alternative data for the most widely used blockchains and decentralized applications.

Token Terminal lets you analyze both blockchains and decentralized applications using the same frameworks applied to traditional marketplace businesses.

With Token Terminal, you can measure and compare the financial performance of these crypto networks using standardized key performance indicators (KPIs) from traditional finance.

> **[Explore standardized onchain data](https://tokenterminal.com/docs/explorer.md)**
>
> Discover the financial and business performance of hundreds of projects.

> **[Create your own dashboards](https://tokenterminal.com/docs/studio.md)**
>
> Build and share your own charts and dashboards using standardized onchain data.

> **[Analyze data in your spreadsheets](https://tokenterminal.com/docs/sheets.md)**
>
> Analyze standardized onchain data directly in your Excel and Google Sheets.

> **[Access metrics via API](https://tokenterminal.com/docs/api-reference)**
>
> Get started with querying standardized onchain data programmatically.

> **[Query the full data catalog](https://tokenterminal.com/docs/catalog/index.md)**
>
> Query raw, decoded, and standardized datasets in your own BigQuery project with Datashare.


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