# Financial Datasets AI MCP

```json
{
  "name": "Financial Datasets AI MCP",
  "slug": "financial_datasets_ai_mcp",
  "url": "https://composio.dev/toolkits/financial_datasets_ai_mcp",
  "markdown_url": "https://composio.dev/toolkits/financial_datasets_ai_mcp.md",
  "logo_url": "https://logos.composio.dev/api/financial_datasets_ai_mcp",
  "categories": [
    "finance & accounting"
  ],
  "is_composio_managed": false,
  "updated_at": "2026-09-29T05:35:54.669Z"
}
```

![Financial Datasets AI MCP logo](https://logos.composio.dev/api/financial_datasets_ai_mcp)

## Description

Securely connect your AI agents and chatbots (Claude, ChatGPT, Cursor, etc) with Financial Datasets AI MCP or direct API to query company fundamentals and metrics, fetch SEC filings and filing sections, retrieve historical and real-time stock prices and ownership, and run stock screens and KPI analyses through natural language.

## Summary

Financial Datasets AI MCP is a financial data platform providing company fundamentals, SEC filings, stock prices, ownership, KPIs, news, and macro data.
Use it to let AI agents query, screen, and analyze market and company metrics with reliable, structured data.

## Categories

- finance & accounting

## Toolkit Details

- Tools: 28

## Images

- Logo: https://logos.composio.dev/api/financial_datasets_ai_mcp

## Authentication

- **Dcr Oauth**
  - Type: `custom`
  - Description: Dcr Oauth authentication for Financial Datasets AI MCP.
  - Setup:
    - Configure Dcr Oauth credentials for Financial Datasets AI MCP.
    - Use the credentials when creating an auth config in Composio.

## Suggested Prompts

- Retrieve latest SEC filings for AAPL
- List company fundamentals for S&P500 firms
- Screen stocks by revenue growth and margin

## 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.

## Installation and MCP Setup

### Path 1: SDK Installation

#### Path 1, Step 1: Install Composio

Install the Composio SDK
```python
pip install composio_openai
```

```typescript
npm install @composio/openai
```

#### Path 1, Step 2: Initialize Composio and Create Tool Router Session

Import and initialize Composio client, then create a Tool Router session
```python
from openai import OpenAI
from composio import Composio
from composio_openai import OpenAIResponsesProvider

composio = Composio(provider=OpenAIResponsesProvider())
openai = OpenAI()
session = composio.create(user_id='your-user-id')
```

```typescript
import OpenAI from 'openai';
import { Composio } from '@composio/core';
import { OpenAIResponsesProvider } from '@composio/openai';

const composio = new Composio({
  provider: new OpenAIResponsesProvider(),
});
const openai = new OpenAI({});
const session = await composio.create('your-user-id');
```

#### Path 1, Step 3: Execute Financial Datasets AI MCP Tools via Tool Router with Your Agent

Get tools from Tool Router session and execute Financial Datasets AI MCP actions with your Agent
```python
tools = session.tools
response = openai.responses.create(
  model='gpt-4.1',
  tools=tools,
  input=[{
    'role': 'user',
    'content': 'YOUR_SPECIFIC_PROMPT_HERE'
  }]
)
result = composio.provider.handle_tool_calls(
  response=response,
  user_id='your-user-id'
)
print(result)
```

```typescript
const tools = session.tools;
const response = await openai.responses.create({
  model: 'gpt-4.1',
  tools: tools,
  input: [{
    role: 'user',
    content: 'YOUR_SPECIFIC_PROMPT_HERE'
  }],
});
const result = await composio.provider.handleToolCalls(
  'your-user-id',
  response.output
);
console.log(result);
```

### Path 2: MCP Server Setup

#### Path 2, Step 1: Install Composio

Install the Composio SDK for Python or TypeScript
```python
pip install composio claude-agent-sdk
```

```typescript
npm install @composio/core ai @ai-sdk/openai @ai-sdk/mcp
```

#### Path 2, Step 2: Initialize Client and Create Tool Router Session

Import and initialize the Composio client, then create a Tool Router session for Financial Datasets AI MCP
```python
from composio import Composio
from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions

composio = Composio(api_key='your-composio-api-key')
session = composio.create(user_id='your-user-id')
url = session.mcp.url
```

```typescript
import { Composio } from '@composio/core';

const composio = new Composio({ apiKey: 'your-api-key' });
const session = await composio.create('your-user-id');
console.log(`Tool Router session created: ${session.mcp.url}`);
```

#### Path 2, Step 3: Connect to AI Agent

Use the MCP server with your AI agent (Anthropic Claude or Mastra)
```python
import asyncio

options = ClaudeAgentOptions(
    permission_mode='bypassPermissions',
    mcp_servers={
        'tool_router': {
            'type': 'http',
            'url': url,
            'headers': {
                'x-api-key': 'your-composio-api-key'
            }
        }
    },
    system_prompt='You are a helpful assistant with access to Financial Datasets AI MCP tools.',
    max_turns=10
)

async def main():
    async with ClaudeSDKClient(options=options) as client:
        await client.query('YOUR_SPECIFIC_PROMPT_HERE')
        async for message in client.receive_response():
            if hasattr(message, 'content'):
                for block in message.content:
                    if hasattr(block, 'text'):
                        print(block.text)

asyncio.run(main())
```

```typescript
import { openai } from '@ai-sdk/openai';
import { experimental_createMCPClient as createMCPClient } from '@ai-sdk/mcp';
import { generateText } from 'ai';

const client = await createMCPClient({
  transport: {
    type: 'http',
    url: session.mcp.url,
    headers: {
      'x-api-key': 'your-composio-api-key',
    },
  },
});

const tools = await client.tools();
const { text } = await generateText({
  model: openai('gpt-4o'),
  tools,
  messages: [{
    role: 'user',
    content: 'YOUR_SPECIFIC_PROMPT_HERE'
  }],
  maxSteps: 5,
});

console.log(`Agent: ${text}`);
```

## Why Use Composio?

### 1. AI Native Financial Datasets AI MCP Integration

- Supports both Financial Datasets AI MCP and direct API based integrations
- Structured, LLM-friendly schemas for reliable tool execution
- Rich coverage for reading, writing, and querying your Financial Datasets AI MCP data

### 2. Managed Auth

- Built-in OAuth handling with automatic token refresh and rotation
- Central place to manage, scope, and revoke Financial Datasets AI MCP access
- Per user and per environment credentials instead of hard-coded keys

### 3. Agent Optimized Design

- Tools are tuned using real error and success rates to improve reliability over time
- Comprehensive execution logs so you always know what ran, when, and on whose behalf

### 4. Enterprise Grade Security

- Fine-grained RBAC so you control which agents and users can access Financial Datasets AI MCP
- Scoped, least privilege access to Financial Datasets AI MCP resources
- Full audit trail of agent actions to support review and compliance

## Use Financial Datasets AI MCP with any AI Agent Framework

Choose a framework you want to connect Financial Datasets AI MCP with:

- [ChatGPT](https://composio.dev/toolkits/financial_datasets_ai_mcp/framework/chatgpt)
- [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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- [Coinbase Wallet MCP](https://composio.dev/toolkits/coinbase_wallet_mcp) - Coinbase Wallet MCP is Coinbase's wallet service for interacting with onchain accounts. Use it to give agents secure, structured access to inspect wallets and request onchain actions.
- [Coinranking](https://composio.dev/toolkits/coinranking) - Coinranking is a comprehensive cryptocurrency market data platform offering access to real-time coin prices, market caps, and historical data. Get accurate, up-to-date stats for thousands of digital assets in one place.
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## Frequently Asked Questions

### Do I need my own developer credentials to use Financial Datasets AI MCP with Composio?

Yes, Financial Datasets AI MCP requires you to configure your own Dcr Oauth credentials. Once set up, Composio handles secure credential storage and management for you.

### Can I use multiple toolkits together?

Yes! Composio's Tool Router enables agents to use multiple toolkits. [Learn more](https://docs.composio.dev/tool-router/overview).

### Is Composio secure?

Composio is SOC 2 and ISO 27001 compliant with all data encrypted in transit and at rest. [Learn more](https://trust.composio.dev).

### What if the API changes?

Composio maintains and updates all toolkit integrations automatically, so your agents always work with the latest API versions.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)