Data Feeder FAQs

Question 1

How does Data Feeder expose data to AI agents?

Accepted Answer

Data Feeder automatically generates MCP 'tools' (callable functions with parameters) or 'resources' (readable endpoints for cached data) directly from your YAML configuration, making external data readily consumable by AI agents.

Question 2

What is Data Feeder?

Accepted Answer

Data Feeder provides declarative data feeds for AI agents. It transforms simple YAML configurations into a fully functional MCP (Model Context Protocol) server, handling data fetching, caching, scheduling, and authentication.

Question 3

How does Data Feeder simplify data access for AI agents?

Accepted Answer

Instead of writing custom code for each API, developers declare data sources in a YAML file. Data Feeder then automatically generates callable MCP tools or readable resources, complete with built-in caching, scheduling, and authentication.

Question 4

What kind of authentication methods does Data Feeder support?

Accepted Answer

Data Feeder supports various authentication types including API key (via header or query parameter), Bearer tokens, and the x402 payment protocol for automated payments to paid APIs.

Question 5

Can Data Feeder cache and prefetch data?

Accepted Answer

Yes, it features built-in in-memory caching with per-source TTL (Time-To-Live) and hit rate statistics. It also supports scheduled prefetching using cron expressions or simple intervals to ensure data is always fresh for agents.

Data Feeder

Data Feeder

Key Features

Use Cases

Key Features

Use Cases