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Tuesday, August 19, 2025

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 normal developed by Anthropic—to attach APIs with trendy giant language mannequin (LLM) interfaces, reminiscent of Claude Desktop.

Immediately, 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 providers immediately by means of conversational interfaces.

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

Why MCP?

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

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

Trusted APIs Powered by AI

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

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

Fast Setup Information

You possibly can 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 instantaneous 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 influence of knowledge applications 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 deal with: John Doe 123 Primary St Boston”
  • “Eating places close to 123 Central Ave”
  • “Wildfire threat for 123 Forest Ln”

Whether or not you’re enriching information, exploring location context, or assessing threat, the MCP server makes it simple 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 skilled help 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 listing of obtainable features to solely these wanted in your particular job. This retains the context lean, and the responses targeted.

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

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

Joyful constructing!

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

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