"terminal" matching MCP servers:

Enables local agents to search and retrieve cited evidence from PDFs and Markdown notes, including page-specific passages and rendered page images.

A fully-local AI memory system that saves notes, code, and terminal output to a vector database via Ollama and LanceDB, enabling context retrieval from terminal, API calls, or inside Claude Code.

Enables developers to index and search their own local PDF, DOCX, Markdown, and text documents from an MCP client or terminal, combining semantic similarity with keyword matching for exact technical terms. Everything runs on the user's machine with no API key, Docker, or external database, so private documents stay private and work offline after the initial model download.

Provides AI coding assistants with context optimization tools including targeted file analysis, intelligent terminal command execution with LLM-powered output extraction, and web research capabilities. Helps reduce token usage by extracting only relevant information instead of processing entire files and command outputs.

Enables Claude to search past coding transcripts stored locally — by exact words or by meaning — read them page by page, and reopen them in a terminal to resume work in the original directory. Everything stays on the machine, with a year-long archive.

Enables AI agents to share a persistent, self-learning memory by ingesting and classifying knowledge from Obsidian, code, transcripts, bookmarks, and terminal sessions, then retrieving token-optimized context through MCP and REST APIs.

Enables natural-language search, listing, and retrieval of 378 curated AI agent, developer-tool, and automation repositories directly from Claude Code, Cursor, Codex, or any MCP-compatible client, returning matching repos with Georgian descriptions. Supports category filtering, result limits, and bilingual (English/Georgian) responses, with an equivalent aipulse CLI for terminal use without an MCP client.

Enables AI agents to share persistent, searchable memory across sessions by ingesting and organizing knowledge from sources like Obsidian, code, transcripts, and terminal sessions. It provides token-optimized retrieval, semantic and keyword search, and MCP/REST access so agents can capture, classify, synthesize, and reuse context.

Enables AI agents to capture, classify, synthesize, and retrieve persistent knowledge from sources like Obsidian, code, web clippings, transcripts, bookmarks, terminal sessions, and fixes, using FTS5 and semantic search through MCP and REST interfaces.

Enables AI agents to share persistent memory by ingesting knowledge from sources like Obsidian, code, documents, web clips, and terminal sessions, then providing token-optimized search, context, classification, synthesis, and capture tools through MCP and REST APIs.

Enables AI agents with persistent memory using SQLite and local LLM models through Ollama integration. Provides chat with context retention and multi-client support across VS Code, Gemini-CLI, and terminal interfaces.

Enables AI agents to capture, classify, synthesize, and retrieve persistent knowledge through MCP and REST APIs, so multiple agents can share memory and improve across sessions. It provides full-text, semantic, and hybrid search with token-optimized context retrieval from sources like Obsidian, code, transcripts, and terminal sessions.

Enables AI agents to capture, classify, promote, synthesize, and retrieve persistent knowledge from sources such as Obsidian vaults, code repos, YouTube transcripts, bookmarks, terminal sessions, and bug fixes. Exposes tiered cold/warm/hot memory with token-optimized search, context briefings, and cross-source synthesis so multiple agents share one compounding brain across sessions.

Enables AI agents to share persistent, self-improving memory by ingesting, classifying, synthesizing, and retrieving knowledge from sources such as Obsidian vaults, code, transcripts, bookmarks, and terminal sessions through MCP and REST APIs.

Gives every AI agent a shared, persistent memory by ingesting Obsidian vaults, code, transcripts, bookmarks and terminal sessions into a three-tier SQLite FTS5 store, then serving token-optimized hybrid retrieval, classification, promotion and cross-source synthesis over MCP and REST. This lets multiple agents read from and write to one compounding brain so each session starts with better context than the last.

Enables AI agents to access a codebase context, select relevant files, and route queries to the appropriate AI model based on complexity, all through an MCP interface.

Enables agents to store documents and atomic facts with provenance in a local SQLite knowledge base, then search them through fused keyword, exact, semantic, entity, graph and fact retrieval arms that return one ranked list where every result carries its address, trust level and a reason for its rank. A read-only terminal and loopback web view let humans inspect the same searches, timelines, entity neighbourhoods and agent-written pages without being able to alter the knowledge base.