Connect AI agents to 6,500+ companies of annual reports and investor relations data

The Quarterlytics MCP server connects AI agents and large language models directly to our database of annual reports and investor relations material across 6,500+ public companies. Built on the Model Context Protocol (MCP) standard, it allows AI tools — including Claude, GPT-based agents, and custom LLM pipelines — to query structured financial data in real time, without hallucination-prone reliance on training data alone. Whether you are building an AI research assistant, an autonomous analyst workflow, or an agentic pipeline that reasons over corporate disclosures, Quarterlytics gives your agent the grounded, structured data it needs to produce accurate, cited answers.

Once connected to the Quarterlytics MCP server, an AI agent can:

• Look up company profiles — retrieve sector, industry, exchange, employee count, and peer companies for any of 6,500+ listed companies across NYSE, NASDAQ, LSE, ASX, and more
• Read annual reports — access structured content from historical filings going back multiple years, cited and ready to reason over
• Analyse investor relations material — pull summaries and key disclosures from public filings on demand
• Browse sectors & industries — query and filter across 10+ sectors and 200+ industries to support comparative and thematic research
• Answer grounded financial questions — combine structured Quarterlytics data with LLM reasoning to produce accurate, source-backed analysis

All data served through MCP is the same cleaned, structured dataset available via the Quarterlytics platform — no scraping, no parsing, no guesswork on your agent's part.

The Quarterlytics MCP server is built for teams using AI to accelerate financial research and analysis. Current users include:

• AI product teams building financial research assistants, chatbots, or copilots that need reliable, structured data grounding
• Quant & data teams running agentic pipelines that reason across large sets of public company disclosures
• Enterprise platforms integrating LLM capabilities into existing financial workflows and tools
• Academics & researchers using AI agents to analyse corporate disclosures, IR trends, or market behaviour at scale

The Model Context Protocol (MCP) is an open standard that lets AI models call external tools and data sources as part of their reasoning process. The Quarterlytics MCP server exposes our dataset as a set of callable tools — so your agent can fetch a company profile, retrieve a filing excerpt, or look up industry peers mid-conversation, exactly when it needs to.

This means your AI workflow stays grounded in real, up-to-date data rather than relying on what a model may or may not have seen during training. It also means your agent can cite its sources — a critical requirement for any financial research use case.

The server is compatible with any MCP-compliant client, including Claude (via Anthropic's desktop and API integrations), OpenAI-based agents using tool calling, and custom LLM orchestration frameworks such as LangChain and LlamaIndex.