Pronto

Pronto Stream is a real time signal engine and Model Context Protocol server designed to feed live data directly into autonomous AI agents, quantitative trading systems, and data engineering pipelines. The platform functions as a low latency wire service that delivers analytical products synthesized from dozens of public data sources, formatted specifically for consumption by large language models and automated systems rather than human analysts scrolling through dashboards. For teams building AI agents that need to reason about current world conditions, Pronto Stream provides the contextual grounding that prevents models from relying solely on their training data cutoff and making confidently incorrect statements about events that happened after their knowledge was frozen.

The platform exposes nine native MCP tools that agents can call directly through a JSON RPC connection, with functions like get_latest_signals and search_signals providing structured access to the data feed. This native MCP integration means AI agents built on frameworks like Claude Desktop or Cursor can query live signals without custom API wrapper code, reducing the engineering effort required to give agents real world awareness. The connection endpoint at pronto.stream/mcp handles authentication and rate limiting automatically, so agents can focus on interpreting signals rather than managing infrastructure concerns or implementing retry logic for failed requests. For developers who have struggled to connect LLMs to live data sources cleanly, the MCP approach offers a standardized solution that several major AI platforms now support natively.

Twenty six cross domain fusion products synthesize information from sources that rarely appear together in conventional data feeds, creating analytical signals that no single data provider offers on its own. The platform combines solar grid loads with seismic activity monitoring, unemployment metrics with maritime vessel tracking, space weather indices with geopolitical threat assessments. Each fusion product applies analytical logic to raw data streams to produce actionable signals rather than raw observations that still require human interpretation. The Grid Carbon Arbitrage Index tracks opportunities in energy markets influenced by renewable generation patterns and grid carbon intensity. The Recession Risk Nowcast combines leading indicators into a continuously updated probability estimate that updates faster than traditional economic reports. The Maritime Chokepoint Risk Score monitors shipping lane disruptions that could affect global supply chains, useful for logistics companies and commodity traders. These products exist because the real value often lies in correlations across domains that siloed data providers never connect.

The Cognitive Wire Format addresses a practical problem that emerges when feeding live data to language models with limited context windows. Standard JSON formatting wastes tokens on structural overhead like field names, brackets, and quotation marks repeated across thousands of records. CWF uses binary compression to reduce context window token consumption by approximately eighty percent compared to equivalent JSON payloads, which translates directly to lower LLM prompt costs and the ability to include more signals in a single query without hitting context limits. For quantitative applications processing thousands of signals per session, this compression makes the difference between viable and prohibitively expensive, especially when using more capable but costly models that charge premium rates per token.

The Hermes Hotplane ingestion system pulls from over fifty four public data endpoints and more than seventy one thousand global RSS news feeds spanning two hundred thirty two countries, providing both structured data and unstructured text signals. Spatial temporal indexing allows agents to query signals by geographic region and time window, useful for applications that need to understand conditions in specific markets or areas rather than processing global noise. The news feed coverage provides early signal on events that take hours or days to appear in structured data sources, giving agents the kind of awareness that human traders gain from monitoring financial news throughout the day. This combination of structured feeds and parsed news creates a richer context than either source provides alone.

Integration support extends beyond MCP to include LangChain, AutoGPT, and custom agent implementations through the underlying JSON RPC 2.0 protocol over standard HTTP connections. This flexibility means Pronto Stream can feed data to whatever agent framework a team has already adopted rather than requiring migration to a specific toolchain or vendor lock in. The platform offers a free developer tier with low latency MCP access for experimentation and prototype development, letting teams validate that the signals actually help their agents before committing to commercial pricing. Production pricing for workloads with higher rate limits and priority support is available through direct contact with the team, though specific tiers and costs require reaching out rather than self service signup through a pricing page.