Recall Data Omnifeeds

Overview

The Recall Data Omnifeeds MCP server unifies access to several high‑value public data sources—Twitter, Substack, and CoinGecko—into a single Model Context Protocol interface. By exposing these feeds through standardized MCP endpoints, the server lets AI assistants such as Claude retrieve, analyze, and act on real‑time content without developers writing custom API wrappers or handling authentication flows for each provider. This streamlines the development of AI‑powered analytics, monitoring tools, or conversational agents that need up‑to‑date social media chatter, newsletter insights, and cryptocurrency market data.

Problem Solved

Developers building AI applications often face fragmented APIs: each platform requires distinct authentication, rate limits, and data schemas. Integrating multiple services can become a maintenance burden, especially when the AI model must respond to user queries that span several domains (e.g., “What’s the latest sentiment on a particular token?”). The Omnifeeds server abstracts these complexities, offering a consistent request/response contract across all supported feeds. This reduces boilerplate code, centralizes credential management, and ensures that AI agents can treat diverse data sources as interchangeable tools.

Core Capabilities

• Twitter: Full read and write access, including user profiles, tweets, trending topics, direct messaging, and even Grok chat integration. This enables AI agents to not only pull data but also interact—tweeting, liking, retweeting, or following—directly from the model’s output.
• Substack: Retrieval of publication metadata, recent posts, comments, and search functionality for both custom domains and subdomains. Ideal for content discovery or sentiment analysis of newsletters.
• CoinGecko: Real‑time token pricing, contract addresses across chains, token search, and trending tokens. Supports both free and Pro API tiers, allowing developers to choose the level of detail required.

Each service is exposed through a set of intuitive MCP tools (e.g., , , ), making it straightforward for an AI assistant to invoke the desired operation and receive structured JSON responses.

Real‑World Use Cases

• Social Listening Bots: An AI assistant can monitor a list of influencers on Twitter, detect emerging trends, and automatically tweet or DM follow‑ups based on sentiment thresholds.
• Market Sentiment Analysis: By combining CoinGecko price data with Twitter chatter, developers can build models that correlate market movements with social media sentiment in real time.
• Content Recommendation Engines: Substack integration allows an AI to surface the most relevant newsletters or posts to users, pulling in comments and engagement metrics for richer recommendations.
• Automated Trading Signals: The server’s write capabilities on Twitter can be leveraged to broadcast trade signals or alerts directly from the AI model.

Integration with AI Workflows

Once registered in a Claude environment, the server appears as a single MCP endpoint. Developers can then invoke its tools within prompts or code snippets, letting the AI orchestrate complex sequences—search for tweets about a token, fetch its current price, and decide whether to tweet an alert—all through the MCP protocol. The unified interface also simplifies testing: a single endpoint can be mocked or replaced without altering the AI’s logic.

Unique Advantages

• Unified Authentication: Credentials for Twitter and CoinGecko are supplied once in a file; the server handles session management internally, freeing developers from dealing with OAuth flows.
• Write‑Enable Twitter: Unlike many data‑fetching MCPs, this server supports full CRUD operations on Twitter, opening possibilities for interactive AI agents that can act on social media.
• Cross‑Domain Search: The ability to search across tweets, Substack posts, and token listings from a single command streamlines multi‑source queries.
• Scalable Architecture: Built with Node.js and modular MCP tooling, the server can be extended to include additional data sources without significant refactoring.

In summary, the Recall Data Omnifeeds MCP server delivers a powerful, developer‑friendly bridge between AI assistants and three of the most dynamic public data ecosystems. By consolidating authentication, rate‑limiting, and schema handling into a single protocol implementation, it accelerates the creation of sophisticated AI applications that can read, analyze, and act across social media, newsletters, and cryptocurrency markets.