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 really talk to your knowledge. Right this moment, 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 not too long ago, for those who needed your AI agent to verify flight costs or lookup a database, you needed to write a customized instrument. When Anthropic launched the Mannequin Context Protocol (MCP), it created a normal manner for Giant 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 instrument and its parameters manually.
3. Deploy and keep that server.
4. Replace the code each time the underlying API modifications.

Agoda staff calls this the ‘integration tax.’ For an organization with 1000s of inner APIs, writing 1000s of MCP servers isn’t sensible. 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 employ APIAgent as a proxy. It sits between your LLM (like Claude or GPT-4) and your current APIs.

The instrument 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 means 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 remainder.

How It Works?

The structure is easy. APIAgent acts as a gateway. When a person asks an AI agent a query, the move seems to be like this:

• The Request: The person asks, ‘Present me the highest 10 motels in Bangkok with essentially the most opinions.’
• Schema Introspection: APIAgent mechanically inspects the API schema to grasp the obtainable 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 consequence again to the LLM.
• The Response: The JSON knowledge travels again by means of APIAgent, which codecs it for the AI to learn.

This method makes use of Dynamic Device Discovery. You’ll be able to level APIAgent at any URL, and it mechanically generates the mandatory 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 reserve it as a ‘Recipe.’

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

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 useful instruments with out guide mapping.
• Superior SQL Submit-Processing: It integrates DuckDB, an in-process SQL engine, to deal with complicated knowledge manipulation. If an API returns 1000’s of unsorted rows or lacks particular filtering, APIAgent makes use of SQL to kind, mixture, or be a part of the info regionally 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 data 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 until a developer explicitly whitelists them within the YAML configuration file.

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