Exevalator v2.4 Released — MCP Support Added, Now Usable as an AI Calculation Tool

We've released Exevalator (an open-source expression-evaluation library) v2.4.0!

• Exevalator — Official Site

We just shipped a major update to v2.3 the other day, which added Python support:

• Nov 4, 2025: Exevalator v2.3 Released — Now Usable from Python

Building on that Python port, v2.4 adds support for the MCP protocol, enabling Exevalator to be used as a calculation tool for AI.

Below, we'll cover the details and show practical examples.

What Is Exevalator?

In its original role, Exevalator is a library you embed in your program to evaluate the value of expressions provided as strings. For example:

When expressions like these are supplied dynamically at runtime (e.g., via user input), handling them correctly can be trickier than it looks.

Exevalator provides this capability as a library across multiple programming languages so you can integrate it easily. It currently supports seven languages: Java, Rust, C++, C#, VB.NET, TypeScript, and Python, with more to come.

And in this release, beyond language bindings, we've added support for MCP, a protocol for connecting AI to external tools and data.

What Is MCP (Model Context Protocol)?

MCP is a relatively new protocol, so many readers may not have run into it yet.

First, a quick refresher: what's a protocol?

What a Protocol Is

A protocol is, roughly speaking, an agreed — upon framework for interaction — the rules and assumptions that let systems communicate and control one another.

Whenever two systems need to exchange information or issue commands, a few things have to be true:

• There must be some sort of conversation rules, or meaningful interaction won't happen at all.
• The parties need to share assumptions — who the other side is, what actions are available, and how state changes in response to those actions.
• You also need contingencies: what if the peer disappears mid-conversation? What happens when a request fails or is invalid?

The collection of such agreements is the protocol. Systems interoperate — communicate, coordinate, and recover — by following it.

MCP: A Protocol for AI Systems to Interact with External Tools and Information

Here's the key point.

AI has advanced rapidly in recent years, and AI systems are now beginning to use tools on their own and carry out tasks autonomously — the rise of so-called AI agents.

In such setups, an AI-agent system interacts with tools for AI (and other information providers):

That interaction needs to be reliable and efficient for AI. A shared protocol makes this far easier.

One strong candidate is MCP (Model Context Protocol), proposed in 2024 by the AI company Anthropic. It's a very fresh protocol, but momentum is building.

With MCP Support, Exevalator Can Now Serve as a Calculation Tool for AI

Exevalator Now Supports MCP

After a bit of background, here's the point: starting with this release, Exevalator supports control via MCP.

As in the earlier diagram, this makes it possible to use Exevalator as a calculation tool for AI:

When Is This Useful?

You might ask:

That's true — if you're willing to let the AI execute arbitrary scripts, you don't need Exevalator at all.

So when does Exevalator help? Precisely when you do not want to grant an AI permission to run arbitrary code.

There are plenty of cases where unrestricted execution is undesirable or outright impossible. For example:

• Environments containing confidential data that must not be accessed.
• Systems where accidental modification could cause catastrophic damage.
• Situations where the AI/LLM has lower reliability and occasionally gets confused or makes odd mistakes.
• Workflows where third-party requests are routed to the AI.

Yet in those same situations, you might still want to allow:

That's where Exevalator shines.

Because of its design, Exevalator lets you compute safely:

• Anything beyond expression evaluation is impossible by construction.
• Only pre-registered functions (implemented or wrapped by you) can be called from expressions.
• You can declare and use variables accessible only within Exevalator — there is no effect on the outside world.

So even if someone tries to read secrets or damage the system, it simply can't be done through Exevalator.

Notes and Limitations

Please keep the following in mind:

• All values are treated as double-precision floating point (i.e., float in Python, double in C-like languages).
• Variables are also double-precision floats only.

Consequently, you cannot create values/instances of other types and pass them as arguments to specific functions. Exevalator is for numerical computation.

Let's Try It!

To wrap up, let's actually connect Exevalator to an AI agent and try it out.

Sample Environment and How to Install/Connect Exevalator

The examples below were run in the following environment:

• OS: Ubuntu Linux 24.04 LTS
• AI agent setup: Visual Studio Code + Cline
• LLM & model used: OpenAI GPT-5 nano (*)

For installation and MCP connection in the setup above, please see the official README.

Try a Computation with Math Functions

First, let's have the agent evaluate an expression that uses some math functions:

The result matches the theoretical value.

Note that the MCP edition of Exevalator comes in two variants:

• exevalator_mcp.py — no functions pre-registered
• exevalator_mcp_math_preset.py — common math functions pre-registered

Here we're using the latter.

In either case, you can edit the source to add more functions, and of course you can implement your own custom functions.

Try a Small Procedural Computation with Variables

Next, let's declare/read/write variables and perform a small, stepwise calculation:

As shown, you can store values for later use.

As an aside, trying this felt like "natural-language programming" in spirit. It might be fun to build an educational language/runtime that leans in this direction — just a thought!

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That's a wrap for this release. Personally, this update was exciting to build — the sci-fi future keeps getting closer!

We plan to expand language support further; tentatively, Go is on the radar for the next major update (v2.5), though this is not final yet.

We'll keep posting Exevalator updates here. Stay tuned!

Author of This Article

Fumihiro Matsui
[ Founder of RINEARN, Doctor of Science (Physics), Applied Info Tech Engineer ]

Develops VCSSL, RINEARN Graph 3D and more. Also writes guides and articles.

| About Me | Contact |

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