"Setting up Calendar and Reminders on Mac" matching MCP servers:

Enables AI assistants to analyze project folders for dependencies and frameworks and fetch current documentation or datasheets from the web.

Parses Unity text-serialized .prefab, .unity, and .asset files to output only Inspector-visible data in hierarchical YAML, reducing token usage by up to 96%.

MCP server that reduces token consumption in AI coding assistants by up to 90% via structural reads, PreToolUse hooks, and tp-\* subagents.

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Enables agents to validate JSON, JSONC, YAML, and TOML configuration files against official SchemaStore schemas, pinpointing errors and likely key misspellings, and to look up schema-backed setting details.

A code review tool server based on Model Context Protocol (MCP), providing multi-dimensional code review and scoring functions.

Enables AI assistants to surgically extract and analyze code, reducing token usage and costs by up to 200x compared to reading entire files.

Enables multi-model AI code review from inside supported coding tools, reporting where up to four models agree and disagree.

Indexes your codebase so AI coding agents can search instead of re-reading files, saving up to 94% of tokens.

Enables AI agents to analyze large codebases and document collections by consulting large context window models (up to 2M tokens) via OpenRouter when files exceed the current agent's context limits.

An MCP server that provides ultra-efficient code exploration through AST analysis, reducing LLM token usage by up to 95% while enabling instant call graph generation and dependency analysis for massive codebases.

An MCP server that folds function and class implementations into structural skeletons while preserving signatures, interfaces, and docstrings, reducing tokens by up to 90%+ for AI agents. Exposes tools to get repository skeletons, unfold specific symbols on demand, and retrieve exact token and prompt cost savings metrics.

Enables checking whether software packages are deprecated, archived, stale, active, or unknown with supporting evidence, auditing dependency manifests, and looking up runtime support and end-of-life dates.

Enables AI assistants to debug .NET programs by launching or attaching to processes, setting breakpoints, stepping through code, inspecting variables, evaluating C# expressions, and handling exceptions, with support for multiple processes and .NET MAUI apps.

MCP server that reduces AI agent token usage by up to 90% through intelligent context compression. Enables efficient code exploration, multi-file refactoring, and debugging by providing tools for smart reading, searching, and managing code context.

Consolidates code understanding, documentation, browser automation, memory, and knowledge graph into a single MCP server with progressive discovery for up to 98% token reduction.

Provides a real API for coding agents to look up design system components, props, and tokens, preventing guessed answers.

Enables coding agents to query breaking API changes in dependencies since a model's training cutoff, retrieve project dependency change summaries, and look up model training cutoff dates.

Provides up-to-date, version-specific documentation and code examples for software libraries directly to LLMs, enabling resolution of library identifiers and retrieval of relevant documentation with code snippets.

Enables AI coding agents to efficiently query code context via a symbol graph, reducing token usage by up to 20x.