"Session" matching MCP servers:

Enables AI agents and developers to dynamically tokenize session context windows and compact dialogue history into structured outputs via MCP, CLI, or Python client. Runs on pure standard library Python with no external dependencies for deterministic, low-latency execution.

Unofficial MCP server for working with Kagi without API access (you'll need to be a customer, tho). Searches and summarizes. Uses Kagi session token for easy authentication.

Delegates replaceable grunt work (boilerplate, formatting, translation, long-document summarizing) from a premium agent to cheap models behind a local LiteLLM proxy, auto-routing each task by type. Delegated calls run in a separate process, so the subscription session and the API credentials never share an environment.

Enables automatic conversation summarization by typing -sun command, which analyzes the current chat session and saves structured summaries as .mdc files with key insights, outcomes, and next steps.

Enables every coding agent session, on every machine you use, to share one permanent word-for-word memory that is folded into a binary tree of one-line summaries, so a fresh session can read the whole history in a fixed-size view and zoom into any line down to the original message. Summaries are produced by your own agents through the server's compaction tools, with no model or API key required, and machines synchronize through a shared folder without any central owner.

Enables integration with Kagi search engine services including web search, content summarization from URLs, and AI assistant conversations. Uses session tokens to access Kagi's search API, summarizer, and AI models directly within MCP-compatible applications.