Developers building LLM-powered agents often encounter the context wall, where the model starts hallucinating or throwing errors due to excessive input. To address this, most developers use naive character counts, but this approach is flawed because tokens don't work like characters. The LLM Token Counter MCP is a tool that helps developers measure token counts precisely, taking into account the specific encoding used by the model provider. The encoding used by the model provider can significantly impact token counts, and using an outdated encoding can lead to undercounting. The LLM Token Counter MCP allows for precise counts across different encodings, including cl100k_base and o200k_base. When building multi-model pipelines, it's critical to consider the token density across different architectures. The tool also accounts for hidden structural delimiters in API templates, which can consume a significant portion of the context window. Proactive truncation and complexity analysis are essential for managing context windows effectively, and the LLM Token Counter MCP provides tools like find_truncation_point and analyze_complexity to help with this. By using the LLM Token Counter MCP, developers can reduce friction and implement token management as a first-class engineering constraint. The tool is available through the Vinkius MCP Catalog and can be easily integrated into agentic workflows, providing a secure and governed way to manage token counts and complexity analysis.