Agoda Open Sources APIAgent to Convert Any REST pr GraphQL API into an MCP Server with Zero Code

Constructing AI brokers is the brand new gold rush. However each developer is aware of the most important bottleneck: getting the AI to truly talk to your knowledge. At present, journey big Agoda is tackling this drawback head-on. They’ve formally launched APIAgent, an open-source instrument designed to show any REST or GraphQL API right into a Mannequin Context Protocol (MCP) server with 0 code and 0 deployments.

The Downside: The ‘Integration Tax‘

Till just lately, for those who wished your AI agent to test flight costs or search for a database, you needed to write a customized instrument. When Anthropic launched the Mannequin Context Protocol (MCP), it created a normal approach for Giant Language Fashions (LLMs) to connect with exterior instruments.

Nevertheless, even with MCP, the workflow is tedious. A developer should:

1. Write a brand new MCP server in Python or TypeScript.
2. Outline each instrument and its parameters manually.
3. Deploy and keep that server.
4. Replace the code each time the underlying API adjustments.

Agoda staff calls this the ‘integration tax.’ For an organization with 1000s of inner APIs, writing 1000s of MCP servers isn’t real looking. APIAgent is their reply to this scaling drawback.

What’s APIAgent?

APIAgent is a common MCP server. As a substitute of writing customized logic for each API, you utilize APIAgent as a proxy. It sits between your LLM (like Claude or GPT-4) and your present APIs.

The instrument is constructed on a particular technical stack:

• FastMCP: Powers the MCP server layer.
• OpenAI Brokers SDK: Handles the language mannequin orchestration.
• DuckDB: An in-process SQL engine used for SQL post-processing.

The ‘magic’ lies in its capacity to grasp API documentation. You present a definition of your API—utilizing an OpenAPI specification for REST or a schema for GraphQL—and APIAgent handles the remaining.

How It Works?

The structure is simple. APIAgent acts as a gateway. When a consumer asks an AI agent a query, the circulation appears like this:

• The Request: The consumer asks, ‘Present me the highest 10 motels in Bangkok with essentially the most critiques.’
• Schema Introspection: APIAgent mechanically inspects the API schema to grasp the out there endpoints and fields.
• The SQL Layer (DuckDB): That is the key sauce. If the API returns 10,000 unsorted rows, APIAgent makes use of DuckDB to filter, kind, and mixture that knowledge regionally through SQL earlier than sending the concise outcome again to the LLM.
• The Response: The JSON knowledge travels again by way of APIAgent, which codecs it for the AI to learn.

This technique makes use of Dynamic Software Discovery. You may level APIAgent at any URL, and it mechanically generates the required instruments for the LLM with out guide mapping.

Key Function: ‘Recipe’ Studying

One of many key options is Recipe Studying. When a posh pure language question efficiently executes, APIAgent can extract the hint and put it aside as a ‘Recipe.’

• These recipes are parameterized templates.
• The subsequent time an identical query is requested, APIAgent makes use of the recipe instantly.
• This skips the costly LLM reasoning step, which considerably reduces latency and value.

Key Takeaway

• Common Protocol Bridge: APIAgent acts as a single, open-source proxy that converts any REST or GraphQL API right into a Mannequin Context Protocol (MCP) server. This removes the necessity to write customized boilerplate code or keep particular person MCP servers for each inner microservice.
• Zero-Code Schema Introspection: The instrument is ‘configuration-first.’ By merely pointing APIAgent at an OpenAPI spec or GraphQL endpoint, it mechanically introspects the schema to grasp endpoints and fields. It then exposes these to the LLM as purposeful instruments with out guide mapping.
• Superior SQL Put up-Processing: It integrates DuckDB, an in-process SQL engine, to deal with advanced knowledge manipulation. If an API returns hundreds of unsorted rows or lacks particular filtering, APIAgent makes use of SQL to kind, mixture, or be part of the information regionally earlier than delivering a concise reply to the AI.
• Efficiency through ‘Recipe Studying’: To resolve excessive latency and LLM prices, the agent options Recipe Studying. It information the profitable execution hint of a pure language question and saves it as a parameterized template.
• Safety-First Structure: The system is ‘Secure by Default,‘ working in a read-only state. Any ‘mutating’ actions (like POST, PUT, or DELETE requests) are strictly blocked by the proxy except a developer explicitly whitelists them within the YAML configuration file.

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