How to Connect AI Agents to Financial Data: MCP vs API vs Skills

A practical comparison of REST API, Claude MCP, and ChatGPT GPT Actions for connecting AI Agents to fear & greed, A-share prices and valuation. Configuration examples and access requirements.

2026-05-16· Updated 2026-10-24

Maintained by FusionIntel Labs · Product documentation and data interpretation

Why agents need financial data

FusionIntel gives AI agents structured financial observations with instrument codes and data dates. As of 2026-10-24, the API covers:

A-share prices and valuation, including P/E and P/B
A-share and HK macro and sector fear & greed
Commodity, ETF, US sector and A-share individual-stock fear & greed, subject to the plan

These are mainly daily datasets. The current product does not offer intraday real-time prices, full financial statements, capital-flow feeds or news sentiment. Agents should report the observation date and missing data instead of treating every response as current-day information.

Three integration paths

This guide compares three ways to call FusionIntel data APIs:

Claude MCP Server — Model Context Protocol exposes tools to compatible clients. The example below uses the local FusionIntel MCP server in Claude Desktop.
Native REST API — Wrap HTTP endpoints as custom tools inside LangChain, AutoGen, Dify, or your own agent framework.
ChatGPT GPT Actions — OpenAI GPTs expose Actions via OpenAPI schema; the GPT calls them mid-conversation.

Downloadable Skills are a separate package of instructions and scripts for compatible clients. A Skill still calls the same API and requires an API key; installing a higher-tier package does not change data entitlement. See integration options.

Path 1: Claude MCP

FusionIntel provides fusionintel-mcp as a versioned package download. Configure Claude Desktop:

{
  "mcpServers": {
    "fusionintel": {
      "command": "npx",
      "args": ["-y", "https://fusionintel.net/downloads/fusionintel-mcp-2.0.2.tgz"],
      "env": { "FUSIONINTEL_API_KEY": "sk_your_key" }
    }
  }
}

After loading the server, confirm that the client lists the FusionIntel tools. Ask for the A-share macro fear & greed series over the past month, then check the selected tool, parameters and returned observation dates. Tool discovery and permission prompts depend on the client.

Pros: Existing tools provide structured names and parameters. Cons: Client capabilities vary; verify that your client supports the required MCP tools and transport.

Path 2: REST API + custom tool

Python HTTP function to wrap in your framework’s tool interface:

import os
import requests
def get_a_stock_feargreed(period: str = "30d") -> dict:
    """Fetch A-share macro fear & greed; period is limited by plan."""
    r = requests.get(
        "https://api.fusionintel.net/v1/feargreed/a_stock_macro/shi_feargreedindex",
        headers={"X-API-Key": os.environ["FUSIONINTEL_API_KEY"]},
        params={"period": period},
        timeout=20,
    )
    r.raise_for_status()
    return r.json()

print(get_a_stock_feargreed())

Pros: HTTP access works with applications that can send requests and parse JSON. Cons: You write the tool description by hand; the model can misuse parameters.

Path 3: ChatGPT GPT Actions

In the ChatGPT GPT Builder, under "Actions", paste the FusionIntel OpenAPI schema URL: fusionintel.net/.well-known/openapi.yaml. Set authentication to "API Key", header name X-API-Key. Full steps: ChatGPT GPT Actions Setup.

Pros: Actions let a configured GPT request data during a conversation. Cons: Availability and publishing options depend on the account and workspace settings. Keep a GPT using your personal API key private; other users could otherwise consume its access and quota.

Side-by-side comparison

Dimension	Claude MCP	REST API	GPT Actions
Client requirement	Compatible MCP client	HTTP and JSON support	GPT editor with Actions enabled
Configuration	MCP server and API key	HTTP request and tool wrapper	OpenAPI schema and authentication
Tool description	Structured	Hand-written	OpenAPI
Access boundary	Configured client	Your application	Configured GPT and its sharing settings
FusionIntel payload	Structured tool results	JSON	API response through an Action

How to choose:

Solo developer + Claude as primary LLM: MCP.
Team + multiple models + own agent framework: REST.
A private conversational workflow in ChatGPT: GPT Actions.
Controlling retries and caching in your own application: REST + caching.

Multi-agent best practices

A workflow can combine a valuation agent and a sentiment agent using FusionIntel data. Keep the observation dates aligned and review the combined output. For that workflow:

Credential handling: keep the API key in a trusted server environment. Calls using the same key share its plan and quota; do not expose it to untrusted agents or public prompts.
Tiered caching: cache authorized responses for your own workflow, and preserve their data dates.
Failure isolation: when an API call fails, the agent must say "data unavailable" rather than hallucinate.
Evidence checks: report instruments, periods and missing values. Do not turn an extreme sentiment reading into a guaranteed trading signal.

Updated 2026-10-24: clarified supported datasets, client requirements and credential boundaries; removed unsupported distribution and multimodal claims.