Text Count MCP Server

The Text Count MCP Server fills a common gap in AI-assisted text processing: the need for reliable, consistent character and word counts. While many language models can approximate these metrics, they often do so with imprecise or inconsistent logic that depends on tokenization heuristics. By exposing dedicated counting tools through the Model Context Protocol, this server guarantees deterministic results that developers can trust in downstream analytics or compliance workflows.

At its core, the server offers two straightforward yet essential utilities. The tool tallies every visible character in a supplied string, including punctuation and whitespace. The tool performs a space-delimited split to determine the number of words, supporting English and other languages that use spaces as word separators. These functions are lightweight, stateless, and can be invoked repeatedly without side effects, making them ideal for batch processing or real‑time user interactions.

For developers building AI experiences, this MCP server becomes a valuable building block. When an assistant needs to report text length constraints (e.g., “your input exceeds 500 characters”) or enforce word limits for content moderation, it can delegate the counting task to this server instead of relying on model‑generated estimates. The deterministic outputs also simplify unit testing and validation, as the results are guaranteed to be identical across environments.

Typical use cases include:

• Content moderation: Verify that user submissions meet character or word limits before acceptance.
• Data quality pipelines: Validate text fields in large datasets, ensuring they conform to predefined size constraints.
• Educational tools: Provide students with precise counts for essay word limits or character‑restricted assignments.
• Compliance reporting: Generate audit logs that include exact text metrics for regulatory purposes.

Integration is seamless with any MCP‑compatible client. A developer can simply send a JSON payload containing the target text and receive a structured response with the count embedded in a human‑readable sentence. This pattern aligns naturally with conversational agents that need to inform users of constraints or report statistics during a dialogue.

While the server currently supports space‑delimited languages and does not handle Japanese word segmentation, its architecture is modular. Adding support for additional tokenization strategies or language‑specific rules can be achieved by extending the tool definitions without altering client code. This extensibility, combined with its precise and predictable behavior, makes the Text Count MCP Server a standout component for any AI workflow that requires reliable text metrics.