AI agents need more structured data they can actually use than raw feeds and scattered timelines.

Tops turns crypto narratives, topic discovery, and social mentions on X into structured data that AI agents can call directly. It’s free to use, requires no API key, and now supports multiple integration paths including MCP, Skill, and API.

Real-time crypto attention is hard to track. Narratives form on X, mentions spike quickly, and new topics can go from niche to market-wide in hours. For AI agents, this signal is difficult to use when it stays buried in noisy timelines, fragmented posts, and unstructured social chatter.

Tops provides a usable layer for that problem.

With Tops, AI agents can:

• identify rising narratives in real time

• inspect a topic through summaries, keywords, and representative posts

• monitor mentions around a project or keyword

• connect social heat with onchain validation

Depending on how you build, there are three simple ways to bring Tops into your workflow.

Also, you can see Tops in action here: https://app.arcade.software/share/9dyyjRiVlINsqLJUesJA

This quick demo walks through how to use Tops data in an AI agent workflow.

Best for AI clients and MCP-compatible tools.

endpoint: https://api.chainbase.com/tops/v1/mcp

Add it with:

claude mcp add --transport http tops <https://api.chainbase.com/tops/v1/mcp>

This is the fastest path if you want an AI client to start querying Tops as part of its toolset.

Best for OpenClaw and Claude Code users.

Install with:

npx skills add <https://github.com/lxcong/web3-data-skill> --skill web3-data

Once installed, you can ask natural language questions like:

What are the hottest narratives in crypto right now?

This is a simple way to make Tops feel native inside your coding workflow.

Best for teams building their own agents and custom pipelines.

Resources:

• CLI: https://github.com/chainbase-labs/cli

• API docs: https://docs.chainbase.com/api-reference/tops-api/tool/list-trending-topics

If you want more control over how your agent scans, ranks, filters, or combines social signals with other data sources, this path gives you the most flexibility.

Tops is built around a small but useful set of tools that cover the main social signal workflows agents need.

Tops becomes much more powerful when it is part of a loop, not just a one-off query. Here are three practical ways to use it well.

Let your agent check list_trending_topics on a schedule, detect new narratives, then automatically call get_topic to dig deeper and produce a summary or report.

This is one of the cleanest ways to build a “what matters now” workflow for research agents, content agents, or internal intelligence dashboards.

Social momentum alone is not enough. The most useful agent workflows connect attention with onchain reality.

A strong pattern is:

1. use Tops to find a fast-rising narrative

2. use Chainbase Web3 MCP or API to check supporting onchain signals such as token holders, volume, or transaction activity

3. let the agent compare social heat with onchain confirmation

That closes the loop from narrative to validation.

Use search_mentions to track a project, keyword, or sector over time. Your agent can monitor changes in attention, run basic sentiment checks, and trigger alerts when conversation shifts.

This is useful for ecosystem teams, traders, and agent workflows that need to stay aware of narrative movement without manually watching X all day.

Tops is not just another dashboard for humans.

It is a way to make crypto attention legible for machines.

That matters because more and more agent workflows will depend on knowing what the market is paying attention to in real time. Not after the fact. Not through screenshots. Not through manually curated lists. Through structured signals they can actually query and act on.

For AI agents in crypto, that is a meaningful upgrade.

If you are building agents that need to understand crypto narratives as they form, Tops is ready to plug in today.

You can start with MCP, install it as a skill, or wire it into your own stack through CLI and REST API.

Explore Tops and start building:

• Product: https://tops.chainbase.com/

• MCP endpoint: https://api.chainbase.com/tops/v1/mcp

• Docs: https://docs.chainbase.com/api-reference/tops-api/tool/list-trending-topics

Chainbase is building the Hyperdata Network for AI — a foundational layer for the DataFi era.

Built as a Hyperdata Network, Chainbase turns onchain signals into structured, verifiable, and AI-ready data that can be directly processed by AI models and decentralized applications. Its core stack includes:

• Manuscript: a programmable layer for building data assets;

• AVS layer: decentralized data execution and verification;

• $C Token: the native currency for AGI.

This structured data layer supports a new generation of crypto applications that are autonomous, composable, and economically aligned with their users and contributors.

To date, Chainbase has indexed over 200 blockchains, processed more than 500 billion data calls, and supports a community of more than 35,000 developers. Over 10,000 projects actively use Chainbase across a wide range of use cases, including MEV infrastructure, L2 explorers, agent protocols, and onchain analytics.

The founding team brings deep experience in blockchain infrastructure, data engineering, and protocol security. Chainbase is backed by top-tier investors and works closely with ecosystems across modular infrastructure, large language models, and onchain AI.

As the need for machine-readable and economically aligned data continues to grow, Chainbase provides the foundational layer for a programmable data economy—one where information moves freely between agents, protocols, and people.

Website | Twitter | Discord | Telegram | Blog | Docs | Github

AI agents need more structured data they can actually use than raw feeds and scattered timelines.

Tops turns crypto narratives, topic discovery, and social mentions on X into structured data that AI agents can call directly. It’s free to use, requires no API key, and now supports multiple integration paths including MCP, Skill, and API.

Real-time crypto attention is hard to track. Narratives form on X, mentions spike quickly, and new topics can go from niche to market-wide in hours. For AI agents, this signal is difficult to use when it stays buried in noisy timelines, fragmented posts, and unstructured social chatter.

Tops provides a usable layer for that problem.

With Tops, AI agents can:

• identify rising narratives in real time

• inspect a topic through summaries, keywords, and representative posts

• monitor mentions around a project or keyword

• connect social heat with onchain validation

Depending on how you build, there are three simple ways to bring Tops into your workflow.

Also, you can see Tops in action here: https://app.arcade.software/share/9dyyjRiVlINsqLJUesJA

This quick demo walks through how to use Tops data in an AI agent workflow.

Best for AI clients and MCP-compatible tools.

endpoint: https://api.chainbase.com/tops/v1/mcp

Add it with:

claude mcp add --transport http tops <https://api.chainbase.com/tops/v1/mcp>

This is the fastest path if you want an AI client to start querying Tops as part of its toolset.

Best for OpenClaw and Claude Code users.

Install with:

npx skills add <https://github.com/lxcong/web3-data-skill> --skill web3-data

Once installed, you can ask natural language questions like:

What are the hottest narratives in crypto right now?

This is a simple way to make Tops feel native inside your coding workflow.

Best for teams building their own agents and custom pipelines.

Resources:

• CLI: https://github.com/chainbase-labs/cli

• API docs: https://docs.chainbase.com/api-reference/tops-api/tool/list-trending-topics

If you want more control over how your agent scans, ranks, filters, or combines social signals with other data sources, this path gives you the most flexibility.

Tops is built around a small but useful set of tools that cover the main social signal workflows agents need.

Tops becomes much more powerful when it is part of a loop, not just a one-off query. Here are three practical ways to use it well.

Let your agent check list_trending_topics on a schedule, detect new narratives, then automatically call get_topic to dig deeper and produce a summary or report.

This is one of the cleanest ways to build a “what matters now” workflow for research agents, content agents, or internal intelligence dashboards.

Social momentum alone is not enough. The most useful agent workflows connect attention with onchain reality.

A strong pattern is:

1. use Tops to find a fast-rising narrative

2. use Chainbase Web3 MCP or API to check supporting onchain signals such as token holders, volume, or transaction activity

3. let the agent compare social heat with onchain confirmation

That closes the loop from narrative to validation.

Use search_mentions to track a project, keyword, or sector over time. Your agent can monitor changes in attention, run basic sentiment checks, and trigger alerts when conversation shifts.

This is useful for ecosystem teams, traders, and agent workflows that need to stay aware of narrative movement without manually watching X all day.

Tops is not just another dashboard for humans.

It is a way to make crypto attention legible for machines.

That matters because more and more agent workflows will depend on knowing what the market is paying attention to in real time. Not after the fact. Not through screenshots. Not through manually curated lists. Through structured signals they can actually query and act on.

For AI agents in crypto, that is a meaningful upgrade.

If you are building agents that need to understand crypto narratives as they form, Tops is ready to plug in today.

You can start with MCP, install it as a skill, or wire it into your own stack through CLI and REST API.

Explore Tops and start building:

• Product: https://tops.chainbase.com/

• MCP endpoint: https://api.chainbase.com/tops/v1/mcp

• Docs: https://docs.chainbase.com/api-reference/tops-api/tool/list-trending-topics

Chainbase is building the Hyperdata Network for AI — a foundational layer for the DataFi era.

Built as a Hyperdata Network, Chainbase turns onchain signals into structured, verifiable, and AI-ready data that can be directly processed by AI models and decentralized applications. Its core stack includes:

• Manuscript: a programmable layer for building data assets;

• AVS layer: decentralized data execution and verification;

• $C Token: the native currency for AGI.

This structured data layer supports a new generation of crypto applications that are autonomous, composable, and economically aligned with their users and contributors.

To date, Chainbase has indexed over 200 blockchains, processed more than 500 billion data calls, and supports a community of more than 35,000 developers. Over 10,000 projects actively use Chainbase across a wide range of use cases, including MEV infrastructure, L2 explorers, agent protocols, and onchain analytics.

The founding team brings deep experience in blockchain infrastructure, data engineering, and protocol security. Chainbase is backed by top-tier investors and works closely with ecosystems across modular infrastructure, large language models, and onchain AI.

As the need for machine-readable and economically aligned data continues to grow, Chainbase provides the foundational layer for a programmable data economy—one where information moves freely between agents, protocols, and people.

Website | Twitter | Discord | Telegram | Blog | Docs | Github