Give your AI agents the right MCP

If your Claude or ChatGPT is not amazing at financial research, it may not be the model's fault. It may be missing the right context and tools.

Human analysts need the right research context to do great work. AI agents do too.

Without a specialized MCP, an agent can usually only search the open web and summarize consumer finance sites. That may be fine for a quick overview, but it is not enough for investment work that depends on buy-side quality context, company-specific debates, and thematic screening across hundreds of names.

CapRelay's MCP gives agents access to the same kind of research context professional investors use inside CapRelay: qualitative screening, buy-side bull and bear cases, earnings reviews, and company-level research across U.S.-listed companies.

A side-by-side example

In a recent demo, we asked the same question to two Claude agents.

What happened to casey's to drive the stock up so much this year? what are other companies that might potentially be exposed to similar positive trends, but hasn't hit their financials yet?

The Claude agent without CapRelay's MCP gave a generic answer about earnings growth and came back with obvious public comps.

The Claude agent with CapRelay's MCP did something more useful. It searched through CapRelay's qualitative screener, buy-side quality bull and bear cases, earnings reviews, and other company research. With that context, it identified fuel margins as a key driver of Casey's performance and surfaced Yesway, a recently public rural convenience store operator with similar exposure.

That is the difference between asking an agent to summarize what is easy to find and giving it the tools to reason from investor-grade research.

Why MCP access changes the output

Agents are strong at using tools, iterating through large lists, and synthesizing structured text. But they still need good inputs.

Without CapRelay, an agent has to reinvent the research process from scratch. It searches the internet, reads whatever is public, and tries to infer what matters. The result is often broad, generic, and weighted toward sources professional investors would not rely on.

With CapRelay's MCP, the agent starts from research that has already been distilled for investment work. It can screen for qualitative traits, pull company-specific debates, and use CapRelay's research pages as structured context. Your agent does not need to spend tokens reading boilerplate when CapRelay has already done the work of organizing the important questions.

What you can ask

CapRelay's MCP is useful anywhere you want an agent to combine research judgment with scale. For example:

• "which companies are approaching a cyclical trough, where industry supply is declining, and management points to a demand recovery?"
• "create a summary table laying out takeover defenses for all software companies below 1b of market cap."
• "find 100 companies above $2b of market cap that have clearly articulated some benefit from using AI internally as part of their business operations."

These are the kinds of tasks where generic web search usually breaks down. CapRelay gives the agent a better starting point.

Available where you already work

CapRelay's MCP is available for Claude.ai, ChatGPT, Claude Code, Codex, Cursor, and other agent workflows. All CapRelay users have access, and higher usage limits are available as enterprise plan options.

If you want your agents to produce work that looks more like human investment research, they need the same high-quality tools and context that human analysts use.

Start a free trial and try CapRelay's MCP out for yourself in minutes.