"Sanity" matching MCP servers:

MCP server for uploading local images to Sanity CMS. Enables AI assistants to upload files from your filesystem directly into Sanity as image assets.

A self-hosted Sanity MCP server with full CRUD, atomic transactions, reference tracking, and advanced tools for content management and operations.

An MCP server that enables Claude Desktop to interact with Sanity.io content, providing tools to create, edit, list documents and get schema templates.

Enables semantic retrieval over a structured Quran and classical exegesis library, returning relevant source chunks and verified references for thematic research queries.

Enables AI agents to check the health of internet infrastructure and specific services, helping diagnose whether issues are local or external.

Enables AI assistants to read an open laser project through LightBurn's local REST API, explain and sanity-check cut settings, change them by natural-language sentence, apply material-library entries, and drop generated SVGs onto the workspace. It also exposes machine state, position, overrides, and the loaded camera overlay for review, while never starting or stopping a job.

An MCP server that gives AI agents the ability to observe and understand images via multi-provider vision, object detection, hierarchical analysis, and color extraction.

An MCP server that provides live fair-market-value price comparisons and Ed25519 signed-receipt verification for x402/MPP services, enabling agents to sanity-check quotes and verify receipts.

An MCP server that lets Claude Code or any MCP client hand bounded work — code review, boilerplate, commit messages, format conversion, embeddings — to a local GPU model, an OpenAI-compatible endpoint, a router, or a cheap cloud API, so those files never enter the orchestrator's context window. This cuts token spend and quota use while providing a second model to sanity-check results.

Enables security teams to determine which ATT&CK techniques lose detection coverage when telemetry sources are removed, by computing set differences over a Sanity content graph and consulting a knowledge base via two Context MCP endpoints.

Lets an MCP client hand bounded work such as code review, boilerplate, commit messages, format conversion or embeddings to a local or cloud OpenAI-compatible LLM, so file contents never enter the orchestrating model's context and token costs drop. It also provides a second model to sanity-check code and drafts while the main assistant keeps planning and tool use.