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LATENT REFERENCES / TAG1

MCP Model Context Protocol

This reference note belongs to Tag1 in Latent References, an archive curated by Keigo Yoshida. Its archive region is Databases. The note preserves its source text and links so that readers can trace the material behind the 3D map.

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Tag1
Archive region
Databases

Archived reference note

English translation of the archived note. JP shows the original text. Source links and literal code are retained; the translation does not update or independently verify the source claims.

MCP (Model Context Protocol) is an open standard technology announced by Anthropic that seamlessly connects AI models (LLMs) with external data and tools. It standardizes connections between different AI tools and databases like “USB-C” and is attracting attention as a technology dramatically improving the efficiency of building AI agents and automating work. Standardized integration: Removes the need for individual development previously required for each API, connecting various tools (Slack, GitHub, databases, etc.) to AI through common procedures. Productivity improvement: AI becomes able to obtain and manipulate real-time external information, making development of agents specializing in particular tasks easier. AI’s “USB-C”: once a tool is created to the MCP standard, any AI model (Claude, ChatGPT, etc.) can use it. Components of MCP MCP host: an AI application interacting with the user (example: Claude for Desktop). MCP client: intermediary between host and server. MCP server: Connectivity to data sources and tools.

Source updated 2026-04-28 · Snapshot 2026-10-08

Source links and calculated neighbors

Cosine values measure shared lexical features, not truth, agreement or identical meaning. Original reference links are labeled separately.

  • ISMIR MIDI DatabaseComputed lexical cosine similarity 0.099 · shared title, text, tags and references
  • Ableton MCP: External ControlComputed lexical cosine similarity 0.090 · shared title, text, tags and references
  • SARSAComputed lexical cosine similarity 0.086 · shared title, text, tags and references