# How to connect Financial Datasets AI MCP with ChatGPT

```json
{
  "title": "How to connect Financial Datasets AI MCP with ChatGPT",
  "toolkit": "Financial Datasets AI MCP",
  "toolkit_slug": "financial_datasets_ai_mcp",
  "framework": "ChatGPT",
  "framework_slug": "chatgpt",
  "url": "https://composio.dev/toolkits/financial_datasets_ai_mcp/framework/chatgpt",
  "markdown_url": "https://composio.dev/toolkits/financial_datasets_ai_mcp/framework/chatgpt.md",
  "updated_at": "2026-09-29T05:35:54.669Z"
}
```

## Introduction

### How to connect Financial Datasets AI MCP with ChatGPT
[ChatGPT](https://chatgpt.com/) can take action across your apps and files, stay with a project for hours, and turn a goal into finished materials like sheets, slides, and docs, not just answers.
In this guide, I will explain the easiest and most secure way to connect your Financial Datasets AI account to ChatGPT via Composio Connect, so it can query company fundamentals, fetch SEC filings and sections, retrieve stock prices and ownership, run screens by financial KPIs, and summarize news and macro data through natural language commands without ever putting your account credentials at risk.

## Also integrate Financial Datasets AI MCP with

- [Claude Cowork](https://composio.dev/toolkits/financial_datasets_ai_mcp/framework/claude-cowork)
- [Hermes](https://composio.dev/toolkits/financial_datasets_ai_mcp/framework/hermes-agent)
- [Atomic Agent](https://composio.dev/toolkits/financial_datasets_ai_mcp/framework/atomic-agent)

## TL;DR

### Why use Composio over default connectors?
- Apps with read and write access. Default connectors mostly can read your data. Composio's Financial Datasets AI integration lets ChatGPT take actions like creating drafts, sending updates, labeling records, and more.
- 1,500+ SaaS toolkits out of the box. Composio gives you instant access to a vast catalog of pre-built connectors, from Gmail and Slack to Notion, Linear, and Salesforce.
- One MCP server for every app. Connect any of your applications on demand through a single endpoint, rather than juggling a separate server for each app.
- Smart, context-aware tool loading. Unlike traditional MCP servers that dump every available tool into the LLM context window, Composio searches for and loads only the tools relevant to the task at hand.
- Cross-app automation. Chain actions across multiple apps in a single run - fetch a thread, summarize it in Notion, and post highlights to Slack without leaving the conversation.

## Connect Financial Datasets AI MCP to ChatGPT

### Prerequisites
- A ChatGPT account. We will use the [ChatGPT desktop app](https://openai.com/chatgpt/download/), and the steps are the same on the web.
- Access to the Financial Datasets AI workspace you want to connect.
- [Composio plugin](https://dashboard.composio.dev/login?utm_source=toolkits&utm_medium=framework_template&utm_campaign=chatgpt&utm_content=composio_connect&next=%2F~%2Forg%2Fconnect%2Fclients%2Fchatgpt).
Note: Composio connects through OAuth. You will be asked to sign in and approve specific permissions. Review the permission screen carefully if you are using a work account.
### Step-by-step: Connect Financial Datasets AI to ChatGPT
### 1. Install the Composio plugin
Click the button. ChatGPT opens the Composio plugin page. Click Install plugin.
[Install in ChatGPT](https://chatgpt.com/plugins/plugin_asdk_app_6a58503580c08191b78cc5bdaf4eba6e)
You can also find it inside ChatGPT: go to Settings, open Plugins, search the marketplace for Composio, and click Install plugin.
### 2. Authenticate
After you install, ChatGPT asks you to authenticate with Composio. Log in and click Allow access to authorize ChatGPT to use your Composio account.
### 3. Start using Composio
That is it. Composio tools are now available in ChatGPT. Type @Composio followed by your request, for example @Composio find unread Financial Datasets AI messages and summarize them, and it will run the actions you authorize. On first use it asks you to authorize Financial Datasets AI.
### Option 2: Add Composio as an MCP server
If you cannot use the plugin marketplace, add Composio as a custom MCP server instead.
### 1. Open the MCP settings
In ChatGPT, go to Settings and open the Plugins section. Inside the Plugins page, click the MCP tab.
### 2. Add the server
Click Add server. In the Name field enter Composio, for Type select Streamable HTTP, and in the URL field paste the Composio MCP server URL:

```bash
https://connect.composio.dev/mcp
```

## What is the Financial Datasets AI MCP server, and what's possible with it?

The Financial Datasets AI MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Financial Datasets AI account. It provides structured and secure access so your agent can perform Financial Datasets AI operations on your behalf.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `FINANCIAL_DATASETS_AI_MCP_GET_BALANCE_SHEET` | Get balance sheet | Retrieves a company's balance sheet, which provides a snapshot of its assets, liabilities, and shareholders' equity at a specific point in time. Essential for assessing a company's financial position. |
| `FINANCIAL_DATASETS_AI_MCP_GET_BENEFICIAL_OWNERS` | Get beneficial owners | Lists beneficial owners (holders of more than 5% of a company's shares, from SEC Schedules 13D/13G) with their filer CIK and reporting-person name. Optionally filter by case-insensitive name prefix. The response's `total` is the full match count; when it exceeds the returned page, narrow the search with `name`. Use this to discover the filer_cik to pass to get_beneficial_ownership. |
| `FINANCIAL_DATASETS_AI_MCP_GET_BENEFICIAL_OWNERSHIP` | Get beneficial ownership | Retrieves beneficial-ownership stakes (holders of more than 5% of a class of shares) from SEC Schedules 13D and 13G. Schedule 13D stakes are ACTIVIST (intent to influence control: proxy fights, board seats, pushing for a sale); Schedule 13G stakes are passive (large asset managers). Query by ticker (who owns this company) OR by filer_cik (what stakes does this owner hold) — exactly one is required. Use type=activist to isolate activist stakes. By default each stake's CURRENT state is returned; set history=true for the full amendment chain. Each row is one reporting person with voting/dispositive powers, percent_of_class, and (for 13D) the stated purpose_of_transaction. Coverage begins January 2025. |
| `FINANCIAL_DATASETS_AI_MCP_GET_CASH_FLOW_STATEMENT` | Get cash flow statement | Provides a company's cash flow statement, showing how cash is generated and used across operating, investing, and financing activities. Key for understanding a company's liquidity and solvency. |
| `FINANCIAL_DATASETS_AI_MCP_GET_COMPANY_FACTS` | Get company facts | Get comprehensive company facts data for a stock ticker or CIK from Financial Datasets. Returns real-time information including market cap, number of employees, sector/industry classification, exchange listing, company location, website URL, SIC codes, weighted average shares, and historical events like ticker changes. |
| `FINANCIAL_DATASETS_AI_MCP_GET_EARNINGS` | Get earnings | Retrieves earnings data from SEC filings. Returns a flat list of `EarningsRecord` entries — same shape in either mode. • COMPANY EARNINGS — pass a `ticker` to get the most recent SEC filings (8-K / 10-Q / 10-K / 20-F) for that company. The same `report_period` may appear in two consecutive entries when an 8-K announcement and the matching 10-Q have both been filed; the 8-K comes first because it filed earlier. Sorted `(report_period DESC, filing_date ASC)`. • EARNINGS FEED — omit `ticker` to get the real-time feed of the most recently filed earnings across all covered companies, sorted by `filing_date` descending and deduped by `(ticker, report_period)`. Each entry exposes `ticker`, `report_period`, `fiscal_period`, `currency`, `source_type`, `filing_date`, `filing_url`, `accession_number`, plus `quarterly` and/or `annual` financial blocks (revenue, EPS, surprise vs estimates, etc.). |
| `FINANCIAL_DATASETS_AI_MCP_GET_FILING_ITEMS` | Get filing items | Retrieves specific sections (items) from a company's SEC filings (10-K, 10-Q, or 8-K). Useful for extracting detailed information such as 'Business', 'Risk Factors', or 'Financial Statements and Supplementary Data'. |
| `FINANCIAL_DATASETS_AI_MCP_GET_FILINGS` | Get filings | Get SEC filings data for a stock ticker or CIK. Returns a list of filings, including the accession number, filing type, report date, and URLs to the filing documents. |
| `FINANCIAL_DATASETS_AI_MCP_GET_FINANCIAL_METRICS` | Get financial metrics | Retrieves historical financial metrics for a company, such as P/E ratio, revenue per share, and enterprise value, over a specified period. Useful for trend analysis and historical performance evaluation. |
| `FINANCIAL_DATASETS_AI_MCP_GET_FINANCIAL_METRICS_SNAPSHOT` | Get financial metrics snapshot | Fetches a snapshot of the most current financial metrics for a company, including key indicators like market capitalization, P/E ratio, and dividend yield. Useful for a quick overview of a company's financial health. |
| `FINANCIAL_DATASETS_AI_MCP_GET_INCOME_STATEMENT` | Get income statement | Fetches a company's income statement, detailing its revenues, expenses, and net income over a reporting period. Useful for evaluating a company's profitability and operational efficiency. |
| `FINANCIAL_DATASETS_AI_MCP_GET_INDEX_FUND` | Get index fund | Get an ETF or index fund's holdings and each position's weight (percent of net assets) for a fund ticker (e.g. SPY), sourced from SEC fund-holdings filings. Returns the fund's latest filing by default, or the composition as of a past date. Response includes a fund header (as-of period, total net assets, coverage counts) and the holdings sorted by weight descending. |
| `FINANCIAL_DATASETS_AI_MCP_GET_INSIDER_OWNERSHIP` | Get insider ownership | Retrieves insider ownership statements for a company: what officers, directors, and 10% owners actually HOLD (common shares, options, RSUs), sourced from SEC Form 3 (an insider's initial statement of ownership) and Form 5 (the annual statement). Complements get_insider_trades, which covers the buys and sells in between: trades are the events, ownership statements are the state. Positions are returned as reported per filing, newest first. |
| `FINANCIAL_DATASETS_AI_MCP_GET_INSIDER_TRADES` | Get insider trades | Retrieves insider trading transactions for a company, including purchases, sales, and other transactions by company officers, directors, and major shareholders. Useful for tracking insider sentiment and ownership changes. |
| `FINANCIAL_DATASETS_AI_MCP_GET_INSTITUTIONAL_HOLDINGS` | Get institutional holdings | Retrieves SEC 13F institutional holdings sourced directly from EDGAR. Query by filer_cik (what positions a filer holds) OR by ticker (which institutional filers hold this security) — exactly one is required. When no report_period filter is supplied, returns the latest available quarter for the filer or ticker. Ticker-mode rows include filer_cik and filer_name per position; filer-mode rows omit those (the filer is already implied by the query). |
| `FINANCIAL_DATASETS_AI_MCP_GET_INSTITUTIONAL_INVESTORS` | Get institutional investors | Lists institutional investors (SEC 13F filers) with their CIK and most recent reported name. Optionally filter by case-insensitive name prefix. Use this to discover the filer_cik to pass to get_institutional_holdings. |
| `FINANCIAL_DATASETS_AI_MCP_GET_INTEREST_RATES` | Get interest rates | Retrieves the latest policy interest rates from major central banks (e.g., FED, ECB, BOE, BOJ). Returns a snapshot of each bank's current rate — no parameters required. Prefer get_macro_data with dataset='interest_rates', which also serves rate history. |
| `FINANCIAL_DATASETS_AI_MCP_GET_KPI_GUIDANCE` | Get kpi guidance | Retrieves forward-looking KPI guidance items issued by company management in earnings releases and SEC filings. Filter by ticker, period (quarterly/annual), metric name, and date range. |
| `FINANCIAL_DATASETS_AI_MCP_GET_KPI_METRICS` | Get kpi metrics | Retrieves historical KPI taxonomy metrics extracted from SEC filings for a company (e.g., subscriber counts, ARPU, units sold). Filter by ticker, period (quarterly/annual), metric name, and date range. |
| `FINANCIAL_DATASETS_AI_MCP_GET_KPI_NON_GAAP` | Get kpi non gaap | Retrieves non-GAAP KPIs reported by a company (e.g., Adjusted EBITDA, Free Cash Flow as defined by the company). Filter by ticker, period (quarterly/annual), metric name, and date range. |
| `FINANCIAL_DATASETS_AI_MCP_GET_MACRO_DATA` | Get macro data | Retrieves macroeconomic datasets. dataset='yield_curve' returns the daily US Treasury par yield curve: one object per business day with the keys date, 1_month, 1_5_month, 2_month, 3_month, 4_month, 6_month, 1_year, 2_year, 3_year, 5_year, 7_year, 10_year, 20_year, 30_year, values in percent. A tenor is null when it was not published that day (for example the 30-year from 2002 to 2006); null is not zero. Omit both dates to get the latest curve as a single object. Give start_date and/or end_date to get history as an array, newest first (default window: the trailing year). dataset='interest_rates' returns central bank policy rates: omit both dates for every major bank's current rate as an array, or give dates plus a bank code (e.g. bank='FED') for that bank's rate history. dataset='inflation' returns US CPI: omit both dates for the latest month of all twelve series, or give dates plus series (e.g. series='cpi_all_sa') for that series' history. Each row has date (the reference month, first day), value (index, 1982-84=100), change_1m_pct and change_12m_pct (percent, 1 decimal; null when the comparison month was not published, e.g. October 2025). The headline inflation rate is change_12m_pct of cpi_all_nsa. dataset='labor' returns the US labor market, i.e. the jobs report (nonfarm payrolls, unemployment rate, participation, hourly earnings), JOLTS job openings, hires, and quits, and weekly jobless claims: omit both dates for the latest print of all 24 series (monthly series on their latest month, weekly jobless claims on their latest week), or give dates plus series (e.g. series='payrolls_sa') for that series' history. Each row has date (the reference period: the first day of the month for monthly series, the week-ending Saturday for weekly), value (in the series' units: thousands of persons, percent, dollars per hour, persons), change_prior and change_year (value minus the prior period and minus the period one year back, same units), and change_year_pct (percent, 1 decimal; null for rates such as unemployment_rate_sa, whose change is in points; null when the comparison period was not published). Useful for the 10-year yield, the 2s10s spread, policy rates, discount rates, real returns, payrolls, the unemployment rate, wage growth, and weekly jobless claims. |
| `FINANCIAL_DATASETS_AI_MCP_GET_NEWS` | Get news | Fetches recent news articles for a specific company or the broad market. Pass a ticker for company-specific news. Call with no arguments for the latest market-moving news, covering AI, macro, rates, earnings, geopolitics, war, energy, crypto, IPOs, housing, and other market-wide topics. Also useful when trying to explain broad price moves — omit the ticker to check for market-wide catalysts. |
| `FINANCIAL_DATASETS_AI_MCP_GET_SEGMENTED_FINANCIALS` | Get segmented financials | Retrieves segment breakdowns from all three financial statement types (income statement, balance sheet, cash flow) in a single call. Returns revenue, operating income, and depreciation by product/segment; assets, goodwill, and long-lived assets by segment; and capital expenditure by segment. Essential for sum-of-the-parts valuation and segment-level analysis. |
| `FINANCIAL_DATASETS_AI_MCP_GET_STOCK_PRICE` | Get stock price | Fetches the most recent price snapshot for a specific stock, including the latest price, trading volume, and other open, high, low, and close price data. |
| `FINANCIAL_DATASETS_AI_MCP_GET_STOCK_PRICES` | Get stock prices | Retrieves historical price data for a stock over a specified date range, including open, high, low, close prices, and volume. |
| `FINANCIAL_DATASETS_AI_MCP_LIST_FILING_ITEM_TYPES` | List filing item types | Provides a list of all available item names that can be extracted from 10-K, 10-Q, and 8-K reports, grouped by filing type. |
| `FINANCIAL_DATASETS_AI_MCP_LIST_STOCK_SCREENER_FILTERS` | List stock screener filters | Retrieves all available filter fields and operators for the stock screener, grouped by category (income statement, balance sheet, cash flow statement, financial metrics, and company attributes like sector and industry). Use this to discover which fields and operators you can use with the screen_stocks tool. |
| `FINANCIAL_DATASETS_AI_MCP_SCREEN_STOCKS` | Screen stocks | Screens and filters stocks by financial metrics, valuation ratios, and company attributes. Combine multiple filter conditions to find stocks matching your investment criteria (e.g., revenue > $1B and P/E ratio < 20). Use list_stock_screener_filters first to see all available fields and operators. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

Once connected, ChatGPT can access the Financial Datasets AI MCP server via Composio to run actions that you authorize, directly in your workspace.

## Complete Code

None listed.

## How to build Financial Datasets AI MCP Agent with another framework

- [Claude Cowork](https://composio.dev/toolkits/financial_datasets_ai_mcp/framework/claude-cowork)
- [Hermes](https://composio.dev/toolkits/financial_datasets_ai_mcp/framework/hermes-agent)
- [Atomic Agent](https://composio.dev/toolkits/financial_datasets_ai_mcp/framework/atomic-agent)

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## Frequently Asked Questions

### What are the differences in Tool Router MCP and Financial Datasets AI MCP?

With a standalone Financial Datasets AI MCP server, the agents and LLMs can only access a fixed set of Financial Datasets AI tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Financial Datasets AI and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with ChatGPT?

Yes, you can. ChatGPT fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Financial Datasets AI tools.

### Can I manage the permissions and scopes for Financial Datasets AI while using Tool Router?

Yes, absolutely. You can configure which Financial Datasets AI scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

### How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Financial Datasets AI data and credentials are handled as safely as possible.

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