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 largest bottleneck: getting the AI to truly talk to your knowledge. At the moment, journey large Agoda is tackling this drawback head-on. They’ve formally launched APIAgent, an open-source device 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, when you wished your AI agent to test flight costs or lookup a database, you needed to write a customized device. When Anthropic launched the Mannequin Context Protocol (MCP), it created a normal means for Massive Language Fashions (LLMs) to connect with exterior instruments.

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

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

Agoda group calls this the ‘integration tax.’ For a corporation with 1000s of inside APIs, writing 1000s of MCP servers will not be lifelike. APIAgent is their reply to this scaling drawback.

What’s APIAgent?

APIAgent is a common MCP server. As an alternative 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 current APIs.

The device is constructed on a selected 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 capability to know API documentation. You present a definition of your API—utilizing an OpenAPI specification for REST or a schema for GraphQL—and APIAgent handles the remainder.

How It Works?

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

• The Request: The consumer asks, ‘Present me the highest 10 resorts in Bangkok with probably the most opinions.’
• Schema Introspection: APIAgent routinely inspects the API schema to know 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, type, and mixture that knowledge domestically through SQL earlier than sending the concise outcome again to the LLM.
• The Response: The JSON knowledge travels again by means of APIAgent, which codecs it for the AI to learn.

This technique makes use of Dynamic Device Discovery. You possibly can level APIAgent at any URL, and it routinely generates the mandatory instruments for the LLM with out handbook mapping.

Key Characteristic: ‘Recipe’ Studying

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

• These recipes are parameterized templates.
• The following time an identical query is requested, APIAgent makes use of the recipe straight.
• 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 inside microservice.
• Zero-Code Schema Introspection: The device is ‘configuration-first.’ By merely pointing APIAgent at an OpenAPI spec or GraphQL endpoint, it routinely introspects the schema to know endpoints and fields. It then exposes these to the LLM as useful instruments with out handbook mapping.
• Superior SQL Publish-Processing: It integrates DuckDB, an in-process SQL engine, to deal with complicated knowledge manipulation. If an API returns hundreds of unsorted rows or lacks particular filtering, APIAgent makes use of SQL to type, mixture, or be part of the info domestically earlier than delivering a concise reply to the AI.
• Efficiency through ‘Recipe Studying’: To unravel 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 ‘Protected by Default,‘ working in a read-only state. Any ‘mutating’ actions (like POST, PUT, or DELETE requests) are strictly blocked by the proxy until a developer explicitly whitelists them within the YAML configuration file.

Try the PR Right here. Additionally, be at liberty to observe us on Twitter and don’t overlook to affix our 100k+ ML SubReddit and Subscribe to our E-newsletter. Wait! are you on telegram? now you possibly can be part of us on telegram as properly.

Michal Sutter is an information science skilled with a Grasp of Science in Information Science from the College of Padova. With a strong basis in statistical evaluation, machine studying, and knowledge engineering, Michal excels at reworking complicated datasets into actionable insights.