"Information about the VIX (Volatility Index)" matching MCP servers:

Enables AI assistants to perform memory forensics analysis using Volatility 3 through natural language prompts. Supports process listing, network connection analysis, and other memory artifact inspection from memory images.

Enables AI assistants to perform Volatility 3 memory forensics through natural language, including process and network analysis, injection detection, and shellcode extraction from memory dumps.

Enables evidence-backed memory forensics through an official Volatility 3 installation, letting clients inventory memory images, discover plugins, run analyses with validated arguments, and read back saved outputs, execution records, and hashes. Runs locally over stdio with no model API key, keeping queries controlled and outputs preserved for citation.

Enables automated memory forensics analysis using Volatility 3, supporting Windows, Linux, and macOS memory dumps through a modular plugin interface.

A personal file-indexing MCP server that provides semantic and keyword search over a file index, plus file content lookup and browsing. It enables agents to search and retrieve files from a user's home directory.

A local, SQLite-backed code index for Claude Code, exposed over MCP, enabling targeted code retrieval without external APIs.

It provides a local static index and query interface for APK/AAR/DEX files, enabling analysis of classes, methods, fields, strings, annotations, and call references for tasks like Xposed module development, reverse engineering, and version comparison.

Enables coding agents to query a local AST-backed index of every git checkout on disk for definitions, callers, references, outlines, imports, and full-text symbol search, returning exact repo/path:line citations in milliseconds. Also supports reindexing on demand and health checks that flag where symbol recall is degraded.

Provides structural, queryable understanding of a Python codebase via MCP tools, enabling direct lookups for callers, dependencies, and class hierarchies without repeated grep/read cycles.

A Model Context Protocol (MCP) server that helps large language models index, search, and analyze code repositories with minimal setup

Enables structured learning with a verified loop: define goals as observable claims, learn through teach-lab-test-gate per claim, and get independently graded by an adversarial examiner to ensure genuine progress.

Provides natural language access to over 100 years of historical market index data, including S\&P 500, NASDAQ 100, and Nikkei 225. It enables users to query price ranges, compare symbol performance, and analyze historical market trends directly within Claude.

Gives Goose/Cursor access to your iOS/macOS project index through the Model Control Protocol (MCP) and IndexStoreDB. This provides exhaustive lists of function call sites to help your agent with refactoring and code navigation.

Enables AI agents to search any local folder through a private SQLite FTS5 index that extracts text from PDFs, Office documents, plain text, code and zip listings, returning ranked matches instead of slow ls/grep scans. It also supports metadata and glob listing, paged retrieval of already-extracted text, duplicate detection, and incremental background re-indexing with secrets and caches excluded by default.

Serves AI agents live, non-RAG access to registered HEP codebases through MCP tools for regex search, symbol lookup, and file reading across C++, Python, Fortran, and Mathematica sources.

Provides local-first retrieval over long design conversations, enabling users to find where a decision was made, why, and what replaced it, with turn and line range citations through hybrid retrieval, typed judgment rerank, exact search, and five MCP tools.

Skill librarian for coding agents: describe a task and get the few matching skills from a large local skill collection, then report whether they worked. Runs locally with Ollama and SQLite.

Enables AI agents to query and recall your entire Gmail inbox locally, providing fast summarization and semantic search without relying on Gmail's search API.

A local-first semantic search server for documents, supporting PDFs, Office files, and text/markdown, enabling natural language search via the Model Context Protocol (MCP).

Enables indexing and semantic search of software project files via MCP, with automatic file watching for updates.

Enables an agent to submit a task brief and receive a short, validated list of skills from its full catalogue that are worth reading before starting.

AST-aware codebase indexing with semantic search, exposed as an MCP server. Enables semantic search and file context retrieval across your codebase using natural language queries.

A Python MCP server for indexing projects using embeddings, providing semantic search to help AI agents navigate codebases.

A local MCP daemon that turns an agentic coding client into a bug bounty operator, with 103 tools for offensive security testing including MITM proxy, traffic analysis, and OOB callbacks.

Governed knowledge base for AI agents via the Model Context Protocol (MCP), enabling agents to search, read, and contribute persisted knowledge with versioning, audit trails, and approval workflows.

Enables offline analysis of MySQL slow query logs, individual SQL statements, and MyBatis mappers to produce deterministic index recommendations and migration SQL without requiring an API key.

A lightweight MCP server that maps keywords to file paths instantly using a JSON index, enabling fast context retrieval for AI agents without embeddings or vector databases.

Enables semantic search across your codebase using Google's Gemini embeddings and Qdrant Cloud vector storage. Supports 15+ programming languages with smart code chunking and real-time file change monitoring.

A structural codebase indexer that exposes 18 tools via the Model Context Protocol for AI-assisted code navigation, enabling efficient querying of functions, classes, dependencies, and call chains without reading entire files.

Indexes local documents (PDF, Word, Markdown, text) into a SQLite database for AI agents to search and retrieve bounded, source-located passages. Runs fully locally with optional OCR, preserving privacy.

MCP server for managing i18n translation files — gives your AI agent full control over your app's translations without dumping entire locale files into context.

Enables coding agents to query AI model API prices, including historical point-in-time lookups with cited sources, using a bundled dated dataset and requiring no API keys.

Self-hosted hybrid code search MCP server with text, symbol, and semantic search layers. Runs locally, no third-party MCP servers, LSP, or SaaS.

A read-only MCP server for E-Hentai and ExHentai, enabling gallery search, metadata retrieval, similar-gallery lookup, tag resolution, and authenticated favorites/archive access.

Enables unified code search and analysis over a monorepo, combining exact, semantic, and call graph queries with indexing, incremental updates, and impact analysis.

A local MCP server that indexes codebases using Tree-sitter AST parsing and LanceDB, enabling AI models to search across repositories with sub-second latency.

A local stdio MCP server that enables researchers to practice explaining their work outside a conference setting through a guided, one-question-at-a-time interview about their research, its audience, its consequences, and a concrete next step. It stores the saved answers in a local SQLite draft and returns an approximate rubric-based grade, without calling any model or requiring an API key.

Provides MCP tools for fetching timestamped YouTube transcripts and top comments, scanning spreadsheets for video links, and a local web UI for grounded video summaries.

Enables local incremental indexing of document folders with offline Cyrillic OCR on CPU, exposing tools to start or cancel indexing, check status, and read page or document transcripts.

Enables MCP clients to read bounded workspace files, validate exact replacement plans for the VS Code extension, and check recent typing progress without modifying source or running shell commands.