Cal-mcp is a Server within the MCP ecosystem, providing AI models with on-demand mathematical expression evaluation. Leveraging the FastMCP framework and Bun runtime, it offers a lightweight yet efficient solution for tasks requiring numerical computation. The server accepts mathematical expressions as input via standard input/output (stdio), parses them using the expr-eval library, and returns the calculated result. It supports basic arithmetic operations and pre-defined constants like E and PI.

This tool addresses the need for AI models to perform calculations without relying on complex, resource-intensive libraries. By integrating cal-mcp, developers can equip their AI models with mathematical capabilities, enhancing their ability to process and respond to complex queries. The server is easily integrated via a simple command-line interface, making it a practical addition to any MCP-enabled AI workflow. Its small footprint and focus on essential mathematical functions make it a valuable asset for developers seeking to augment their AI models with computational power.

Math Expression Evaluation

Cal-mcp's core function is to evaluate mathematical expressions provided as strings. It leverages the expr-eval library to parse and compute these expressions, supporting standard arithmetic operations like addition, subtraction, multiplication, division, and exponentiation. The service also incorporates built-in mathematical constants such as E (Euler's number) and PI, enhancing its utility for scientific and engineering calculations. This feature allows AI models to offload complex mathematical computations to a dedicated service, simplifying their internal logic and improving overall performance.

For example, an AI model predicting stock prices could use cal-mcp to calculate moving averages or apply complex financial formulas. The AI model sends the formula as a string to cal-mcp, which returns the calculated result. This separation of concerns allows the AI model to focus on data analysis and prediction, while cal-mcp handles the computational aspects.

Built-in Constant Support

Cal-mcp enhances mathematical computations by providing pre-defined constants like E (Euler's number) and PI. These constants are directly accessible within mathematical expressions, eliminating the need for users to define them manually. This feature ensures accuracy and consistency in calculations, particularly in scientific and engineering applications where these constants are frequently used. By including these constants, cal-mcp simplifies the process of performing complex calculations, making it more efficient and user-friendly for AI models.

Consider an AI model designed to simulate physical phenomena. It can use cal-mcp to calculate the area of a circle using the formula PI * radius^2. The AI model sends this expression to cal-mcp, which utilizes its built-in PI constant to compute the result accurately. This eliminates the need for the AI model to store or define the value of PI, streamlining the calculation process.

FastMCP Framework Integration

Cal-mcp is built on the FastMCP framework, enabling seamless integration with other components in the MCP ecosystem. This integration provides a standardized interface for AI models to interact with cal-mcp, ensuring compatibility and simplifying the development process. The FastMCP framework handles the communication and data transfer between the AI model and cal-mcp, allowing developers to focus on the core functionality of their applications. This tight integration also facilitates the creation of complex workflows where multiple MCP components work together to solve intricate problems.

For instance, an AI-powered chatbot could use cal-mcp to answer mathematical questions posed by users. The chatbot, acting as an MCP client, sends the user's question (e.g., "What is the square root of 9?") to cal-mcp via the FastMCP framework. Cal-mcp evaluates the expression and returns the result to the chatbot, which then presents the answer to the user. The FastMCP framework ensures that this interaction is smooth and efficient, regardless of the underlying technologies used by the chatbot and cal-mcp.