MCP Character Counter

Overview

The MCP Character Counter is a lightweight server that exposes a single, highly useful tool to AI assistants: . It accepts arbitrary text and returns a granular breakdown of its character composition, including total characters, characters without spaces, letters (both uppercase and lowercase), numbers, and symbols. By providing this data in a structured format, the server empowers developers to incorporate precise text analytics into conversational workflows without building custom parsers from scratch.

Solving a Common Need

In many natural‑language processing and data‑validation scenarios, knowing the exact composition of a string is essential. For example, form validation often requires that an input contain a minimum number of letters and digits, or email‑address generators must avoid certain symbols. Traditional methods involve writing ad‑hoc regexes or iterating over strings manually, which can be error‑prone and difficult to maintain. The MCP Character Counter abstracts these concerns into a single, reusable service that can be called from any MCP‑compatible client.

Value for AI‑Powered Development

When integrated into an assistant such as Claude or GitHub Copilot, the counter lets developers query character statistics on the fly. A developer can simply ask the assistant to “Count the characters in this paragraph” or “How many numbers are in ‘123 Main St, Apt 45B’?” and receive an immediate, machine‑readable response. This reduces context switching between the assistant and external tools, streamlines debugging of text‑processing logic, and accelerates prototyping by providing instant feedback on string characteristics.

Key Features in Plain Language

• Total character count – the length of the input string, including all whitespace and punctuation.
• Non‑space count – excludes spaces to reveal the density of meaningful content.
• Letter count – tallies alphabetic characters (a‑z, A‑Z) for language‑specific analyses.
• Number count – counts digits (0‑9), useful for numeric validation or extraction tasks.
• Symbol count – counts any non‑alphanumeric character, helping detect unwanted symbols or formatting.
• Detailed breakdown – returns all counts in a single JSON object, making it easy to consume programmatically.

Real‑World Use Cases

• Form validation – automatically verify that user input meets length and composition requirements before submission.
• Data cleansing – identify strings with excessive symbols or missing numeric data in datasets.
• Marketing analytics – analyze tweet or comment character distributions to optimize messaging within platform limits.
• Security auditing – detect suspicious patterns in passwords or identifiers by inspecting character composition.

Integration into AI Workflows

Developers add the server to their MCP client configuration, after which a hammer icon appears in the assistant’s input area. The assistant can then invoke the tool via natural language or explicit commands (e.g., ). The server runs as a lightweight Node.js process, ensuring minimal overhead while delivering quick responses. Because the output is structured JSON, downstream applications can immediately parse and act on the data—whether that’s displaying a summary to the user, feeding it into another tool, or triggering conditional logic in a larger workflow.

Standout Advantages

• Zero configuration for the assistant – once the server is registered, no additional code changes are required in the AI client.
• Cross‑platform compatibility – works seamlessly with Claude Desktop, GitHub Copilot, and any other MCP‑aware tool.
• Extensibility – developers can modify the underlying script to add new metrics (e.g., uppercase vs. lowercase counts) without altering the MCP interface.
• Performance – operates in constant time relative to input length, making it suitable for real‑time interactions.

In summary, the MCP Character Counter transforms a routine text‑analysis task into an instant, AI‑driven service. It frees developers from repetitive parsing logic, enriches conversational assistants with actionable data, and fits naturally into existing MCP workflows—making it a valuable addition to any AI‑centric development toolkit.