MCPBench is a framework for evaluating the performance of MCP servers. It supports the evaluation of two types of tasks: web search and database query, is compatible with local and remote servers, and mainly evaluates accuracy, latency, and token consumption.

The MCP Text Editor Server is a text file editing service based on the Model Context Protocol, providing line-oriented text file operation capabilities and supporting efficient partial file access to reduce token consumption for LLM tools. It has features such as concurrent editing detection, multi-file atomic operations, and support for multiple encodings.

An MCP service that reduces token consumption in language model interactions through efficient data caching

OneTool is a unified MCP server that exposes over 100 tools through the Python API, significantly reducing token consumption and cost when AI calls tools, avoiding context pollution, and supporting multiple functions such as web search, database, file operations, and chart drawing.

An MCP service that reduces token consumption in language model interactions through a caching mechanism

A tool based on the MCP protocol for querying and using the GitHub GraphQL API, providing functions such as precise data retrieval and reduced token consumption, and supporting multiple languages.

TOON-MCP is a model context protocol server that can reduce token consumption by up to 60% in AI-assisted development workflows by automatically converting verbose JSON structures into the Token-Optimized Object Notation (TOON).

MCP Funnel is a proxy server that can aggregate multiple MCP servers into a single interface. Through refined tool filtering, it significantly reduces context token consumption, supports dynamic discovery, and enables tools on demand.

OmniMCP is a semantic routing tool that centrally manages multiple MCP servers through a single interface. It solves the context bloat problem caused by traditional MCP tool definitions, enables on - demand dynamic loading of tools, and significantly reduces token consumption.

MCP BatchIt is a batching server for MCP tool calls. By aggregating multiple MCP tool calls into one request, it significantly reduces the communication overhead and token consumption of AI agents.

A CLI tool and MCP server for performing CRUD operations on xcstrings (string catalog) files, providing targeted localization key - value management and avoiding excessive token consumption when AI assistants process large files.

DMCP is a semantic tool discovery protocol that solves the problem of too many tools in MCP servers. It realizes on-demand query-driven tool discovery through vector search, significantly reducing token consumption and improving the accuracy of tool selection.

A memory cache server based on the MCP protocol that reduces token consumption by efficiently caching language model interaction data, supporting automatic management and configuration optimization.

An optimized Playwright MCP server that uses a client-server architecture to achieve browser automation. Through an innovative semantic snapshot algorithm, it significantly reduces the amount of page content transmission, while retaining key elements and reducing token consumption by 90%.

NCP is an intelligent orchestrator for the MCP protocol that manages multiple MCP servers in a unified manner. It automatically routes tool calls through semantic understanding, significantly reducing the cognitive burden and token consumption of AI tools, and improving the work efficiency and response speed of AI assistants.

An MCP server that optimizes token usage in Claude Code by automatically compressing JSON, Markdown, and code files, reducing token consumption when reading files.

A semantic compression tool that converts numerical data into natural language descriptions, optimizing the processing efficiency of LLMs and reducing token consumption.

slack-cli-mcp is a project that encapsulates the Slack MCP server into a Docker command-line interface, aiming to significantly reduce the token consumption of LLM agents and improve efficiency through the CLI method.

An MCP server that allows Claude to perform multiple find-and-replace operations in a single tool call, ensuring atomicity - all edits are either applied successfully or not applied at all, significantly reducing the number of tool calls and token consumption.

A code navigation MCP server based on semantic search, achieving efficient code retrieval through AI embedding and AST parsing, and reducing token consumption

An efficient server management tool based on GitLab MCP that significantly reduces token consumption through the progressive disclosure model and provides comprehensive support for GitLab API operations.

OneMCP is a universal MCP protocol aggregator that uniformly manages tools from multiple external MCP servers through a meta-tool architecture. It uses progressive discovery and semantic search technologies to significantly reduce token consumption of LLMs and improve tool discovery efficiency.