Make FinFeedAPI Data AI-Ready with MCP

Connect AI agents directly to financial data across markets, filings, and datasets using a unified, tool-based interface instead of custom API integrations.
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What is MCP & What You Can Do?

Model Context Protocol (MCP) is a standard that exposes APIs as structured tools with defined schemas. Clients can discover available tools, validate inputs, and execute requests in a consistent way. Each FinFeedAPI endpoint is mapped to a tool, making data access predictable and machine-readable. With this approach, you can:

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Discover Available Data
List exchanges, assets, symbols, markets, datasets, filings, and storage structures before making requests.
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Query Structured Data
Retrieve OHLCV candles, exchange rates, filings, market activity, order books, and dataset metadata through schema-defined tools.
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Build Agent Workflows
Let AI agents understand available operations, validate inputs, and request financial data across multiple domains in a consistent way.

How MCP Changes API Usage

No manual request building or response parsing

Tools are discovered automatically by the client

Parameters are checked before execution

Calls are structured, not prompt-based

Responses are ready to use without transformation

Why MCP Servers Are the Choice for You

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Self-Describing APIs
Each MCP server exposes tools with JSON schemas, so clients can understand available operations, required parameters, and response formats without manual setup.
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Single Authentication Model
Use the same X-APIKey header across all MCP servers, with no changes between products or environments.
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No Integration Layer
Connect directly to hosted MCP endpoints. No SDKs, wrappers, or custom adapters are required to start querying data.
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Unified Data Model
Work with consistent structures across stocks, currencies, filings, datasets, and prediction markets, making it easier to reuse logic across use cases.
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Built for AI Environments
Designed for MCP-compatible tools like Cursor, Claude Desktop, and agent frameworks that rely on structured tool calling.
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Always Up to Date
New tools and datasets are automatically exposed through the MCP manifest, so your integration stays current without versioning or manual updates.

Example MCP Configuration

  1. Connect your MCP client to a FinFeedAPI endpoint

  2. Authenticate using your API key (X-APIKey)

  3. Let your client discover available tools automatically

  4. Start with metadata (exchanges, assets, symbols, markets)

  5. Query data (rates, OHLCV, filings, order books, datasets)

FinFeedAPI MCP servers work with: Cursor, Claude Desktop, OpenAI agents, and Custom MCP-compatible runtimes. 

1{
2  "mcpServers": {
3    "FinFeedAPI-Stock-Historical": {
4      "url": "https://api-historical.stock.finfeedapi.com/mcp",
5      "headers": {
6        "X-APIKey": "YOUR_API_KEY_HERE"
7      }
8    },
9    "FinFeedAPI-Currencies-Realtime": {
10      "url": "https://api-realtime.fx.finfeedapi.com/mcp",
11      "headers": {
12        "X-APIKey": "YOUR_API_KEY_HERE"
13      }
14    },
15    "FinFeedAPI-SEC": {
16      "url": "https://api.sec.finfeedapi.com/mcp",
17      "headers": {
18        "X-APIKey": "YOUR_API_KEY_HERE"
19      }
20    }
21  }
22}

Available MCP Servers

MCP ServerEndpointWhat You Can Access
Stock Historical MCPhttps://api-historical.stock.finfeedapi.com/mcpExchanges, symbols, OHLCV, native IEX datasets
Currencies Realtime MCPhttps://api-realtime.fx.finfeedapi.com/mcpCurrent FX and crypto exchange rates
Currencies Historical MCPhttps://api-historical.fx.finfeedapi.com/mcpHistorical rates and OHLC timeseries
SEC MCPhttps://api.sec.finfeedapi.com/mcpFiling search, extraction, downloads, XBRL → JSON
Flat Files MCPhttps://mcp.flatfiles.finfeedapi.com/mcpBuckets, prefixes, dataset discovery
Prediction Markets MCPhttps://api.prediction-markets.finfeedapi.com/mcpMarkets, trades, order books, OHLCV

Why use MCP instead of a standard API integration?

Traditional APIs require manual integration, validation, and parsing at every step. Model Context Protocol replaces this with structured, self-describing tools that AI agents can discover and use instantly. No custom integrations, no guesswork just consistent, reliable data access designed for automation and AI-driven workflows.
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Frequently Asked Questions
What is Model Context Protocol (MCP)?

Model Context Protocol (MCP) is a protocol that allows AI agents to interact with APIs as structured tools. Instead of relying on prompts, agents can discover available operations, validate inputs, and request data in a consistent and reliable way.

How does MCP work with FinFeedAPI?

FinFeedAPI MCP servers expose APIs as tools with defined JSON schemas. Your MCP client connects to an endpoint, loads available tools, and allows your AI agent to call them directly to retrieve financial data.

What data can I access through FinFeedAPI MCP servers?

You can access a wide range of financial data, including stock OHLCV data, FX and crypto exchange rates, SEC filings, prediction market data, and flat file datasets all through structured MCP tools.

Do I need to build a custom integration to use MCP?

No. MCP removes the need for custom integrations. You connect your MCP client to a FinFeedAPI endpoint, and the available tools are discovered automatically.

Which AI tools support MCP?

FinFeedAPI MCP servers work with MCP-compatible environments such as Cursor, Claude Desktop, OpenAI agent frameworks, and custom runtimes that support tool-based API interaction.

How do I authenticate with FinFeedAPI MCP servers?

Authentication is done using your FinFeedAPI API key. You include it in the X-APIKey header when configuring your MCP client.

What is the advantage of MCP over traditional APIs?

MCP provides structured, self-describing tools instead of raw endpoints. This allows AI agents to understand available operations, validate inputs automatically, and use responses without manual parsing.

Can I use MCP for real-time and historical data?

Yes. FinFeedAPI MCP servers support both real-time and historical data, including live exchange rates, historical OHLCV candles, filing archives, and dataset-based workflows.

How do AI agents discover available data through MCP?

MCP clients load tool definitions from the server, including available operations, parameters, and response formats. This allows agents to explore data without prior knowledge of endpoints.

What types of data can I access through MCP?

You can access exchanges, assets, symbols, markets, datasets, filings, and time series data, depending on the MCP server you connect to.

Do I need to know endpoints before using MCP?

No. MCP removes the need to manually know or remember endpoints. Tools are discovered automatically and include all required parameters.

How are MCP tools organized?

Tools are grouped by functionality, such as discovery (e.g. assets, exchanges), data queries (e.g. rates, OHLCV), or dataset access.

What happens if no data is available for a request?

The MCP server returns either an empty result or a clear error message, depending on the tool and dataset.