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Constructing an AI-First Interface for Exactly APIs with Mannequin Context Protocol


Over the previous few weeks, I’ve been exploring methods to streamline entry to Exactly’s APIs utilizing AI-first tooling. One promising strategy has been to leverage the Mannequin Context Protocol (MCP)—an open customary developed by Anthropic—to attach APIs with fashionable giant language mannequin (LLM) interfaces, corresponding to Claude Desktop.

Right now, I’d wish to share a light-weight setup that allows builders—and even non-developers—to work together with our APIs utilizing pure language prompts. This strategy eliminates the necessity for writing boilerplate code, permitting for intuitive exploration of our companies immediately by way of conversational interfaces.

Constructing an AI-First Interface for Exactly APIs with Mannequin Context Protocol

Why MCP?

MCP presents a standardized technique for AI functions to attach with APIs, information, and instruments. It gives a structured strategy to describe features and parameters, enabling LLMs to dynamically resolve which features to invoke in response to consumer prompts.

This aligns with our broader objective at Exactly: making it simpler to combine high-integrity information with functions and workflows. With MCP, we cut back the friction concerned in connecting to our APIs, opening new potentialities for speedy prototyping and experimentation.

Trusted APIs Powered by AI

To display this, I constructed an MCP server that wraps all of the obtainable endpoints from Exactly APIs. The result’s a code-light surroundings the place Claude Desktop can execute API calls mechanically primarily based on a consumer’s request—no guide coding required.

The method of wrapping the APIs wasn’t tough, however it was detailed. Every endpoint was transformed right into a callable operate MCP may expose. As soon as related, Claude Desktop can perceive these features and start utilizing them primarily based on consumer directions.

Fast Setup Information

You may shortly stand up and operating with the MCP server. This repository contains all the pieces you want: a listing of supported API features, detailed set up directions, authentication setup, and pattern requests that will help you get began.

Key Advantages of an MCP Server

Our MCP server is designed to take away the standard friction concerned in API integration, providing a easy, scalable bridge between high-quality spatial information and AI interfaces. It helps pure language prompts and permits on the spot entry to location intelligence instruments and wealthy datasets—with out requiring any code. This lowers the barrier to entry for experimentation and dramatically reduces time-to-value.

Its AI-first design shifts focus away from backend system complexity, permitting groups to focus on fixing real-world challenges. And since it opens API entry to product managers, analysts, and different non-engineering customers, it helps scale the impression of knowledge packages throughout the group—with out including to developer workload. This makes it simpler than ever to show concepts into working options in minutes, not days.

These are only a few examples of the sorts of pure language prompts the MCP server can deal with:

  • “Parse this handle: John Doe 123 Predominant St Boston”
  • “Eating places close to 123 Central Ave”
  • “Wildfire danger for 123 Forest Ln”

Whether or not you’re enriching information, exploring location context, or assessing danger, the MCP server makes it straightforward to get solutions immediately—with out writing a single line of code.

Developer portal

Knowledge Integrity Suite Developer Portal

Speed up your developer journey – Our greatest-of-breed APIs empower builders to ship distinctive experiences and groundbreaking functions on time, each time! With complete documentation and knowledgeable assist at your fingertips, you’ll have all of the instruments to deliver your imaginative and prescient to life.

Finest Practices

The MCP server contains many callable features by default. To enhance efficiency and guarantee mannequin readability, it’s greatest to restrict the checklist of accessible features to solely these wanted to your particular process. This retains the context lean, and the responses targeted.

I hope you take pleasure in making an attempt out this setup and seeing how straightforward it may be to work together with our APIs utilizing pure language!

When you haven’t already, I additionally encourage you to take a look at our Developer Portal and discover the total vary of Exactly APIs obtainable. There’s much more you are able to do—and now it’s even simpler to get began.

Completely happy constructing!

The put up Constructing an AI-First Interface for Exactly APIs with Mannequin Context Protocol appeared first on Exactly.

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