Pronto

Pronto is a real-time data platform that delivers physical, financial, and environmental signals to AI agents through a Model Context Protocol server. It aggregates data from 54 public endpoints and over 71,000 RSS feeds, then exposes that data through a set of native tools that AI assistants can query directly. The target audience is autonomous agents, quantitative funds, and anyone building systems that need fresh signals across multiple domains without stitching together a dozen different APIs.

The core pitch is about token efficiency. Traditional data feeds return verbose JSON payloads that chew through an AI model's context window. Pronto uses a Cognitive Wire Format that the team claims reduces token consumption by around 80 percent compared to raw JSON. If you're running agents that make repeated data calls within a conversation, that compression matters because it leaves more room for reasoning and follow-up queries.

The data itself spans several domains. There are market signals, climate and weather data, maritime shipping indicators, energy grid metrics, and macroeconomic nowcasts. Pronto packages these into what it calls fusion products, pre-built indices like a Grid Carbon Arbitrage Index, a Recession Risk Nowcast, and a Maritime Chokepoint Risk Score. There are 26 fusion products in total, designed to surface cross-domain patterns that would be tedious to compute from raw feeds yourself.

Under the hood, the platform uses an ingestion engine called Hermes that does spatio-temporal indexing. What that means in practice is that queries can filter by location and time window, which is useful for signals that vary geographically, like energy prices or shipping delays. The MCP server exposes nine native tools covering signal queries, search, fusion product access, and snapshot retrieval.

Integration is aimed at AI-native workflows. The documentation references Claude Desktop, Cursor, LangChain, and AutoGPT as compatible clients. You connect Pronto as an MCP server, and then your agent can call its tools as part of a larger reasoning loop. For teams building autonomous systems that need to act on real-world conditions, having a single endpoint for diverse signal types simplifies the data plumbing.

The developer tier is free and provides low-latency access to the MCP tools. Enterprise pricing isn't detailed publicly, but the positioning suggests a self-serve free tier for exploration and integration testing. If you're evaluating whether this fits your use case, you can connect and query without upfront commitment.

One limitation is that the platform is built for agents, not human dashboards. There's no charting interface or exploration UI. You connect, query, and process the results programmatically. For teams that already operate in that mode, it's a feature. For those who want to browse data visually before committing, it may require a wrapper.