"Information about ImGui (Immediate Mode GUI library)" matching MCP servers:

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.

Context Mode is an MCP server that reduces context window waste by sandboxing data-heavy tools, tracking session state in SQLite, and promoting code-based analysis over raw data reads, achieving up to 98% context savings.

This MCP server enables AI agents to search and retrieve exact, cited passages from a large corpus of public-domain books, including full-text search, book metadata, chapters, quotes, and 'ask book' Q&A. Payments are handled via x402 micropayments on Base.

Enables locally-first literature research through an MCP host by collecting sources, parsing documents, retrieving evidence, validating citations, and exporting research records in multiple formats.

Provides a 'library keeper' sub-agent that ingests PDF/EPUB books and answers questions grounded in them via local embedding search, with deterministic classification and knowledge gap logging.

Enables a persistent local library for mathematical PDFs with cached LaTeX extraction, full-text search in English and Greek, and page/image retrieval over MCP.

Turns a user's own Markdown wiki into a locally indexed knowledge base, enabling semantic search, per-page retrieval, source listing, and citation-backed RAG question answering. All embedding and generation run through a local Ollama instance, so queries and documents never leave the machine.

Local MCP server that provides verified answers about Jimmer and internal libraries using a SQLite FTS5 knowledge base.

Provides token-efficient semantic search and document retrieval by indexing PDFs, text, and markdown files into local notebooks using ChromaDB. It enables AI agents to query relevant passages from large documents through local embedding models like Hugging Face or Ollama.

Enables users to search and retrieve research datasets via natural language queries, with tools for proposing and committing tag changes. Includes robust authorization, idempotent mutations, and an audit trail.

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

An MCP server that fetches real-time documentation for popular libraries like Langchain, Llama-Index, MCP, and OpenAI, allowing LLMs to access updated library information beyond their knowledge cut-off dates.

Enables AI agents to create, search, and manage subject matter expert profiles, including private workspace organization and moderated sharing to a common library.

Enables fully local retrieval over a personal document corpus via hybrid search, cross-encoder reranking, RAPTOR summaries, and knowledge graph queries, served to AI agents over MCP.

Enables offline CRUD and semantic search on Wikipedia ZIM archives via MCP tools for reading, writing, editing, deleting, and searching articles.

Provides AI agents with persistent knowledge storage, enabling them to store, search, and retrieve text, documents, and files using semantic and keyword search via MCP tools.

Bridges AI agents with watsonx.data document libraries, enabling natural language queries and intelligent retrieval from document libraries.

Provides AI assistants with intelligent access to ML textbook content for creating accurate, source-grounded documentation using local models for privacy and cost efficiency.

Provides AI assistants with intelligent access to ML textbook content through RAG-powered search, chapter retrieval, and documentation generation capabilities. Uses local models for privacy-focused, source-grounded responses from indexed technical books.

A FastMCP server that enables AI assistants to extract structured information from unstructured text using Google's langextract library through a secure, optimized Model Context Protocol interface.