"Debugging software using GDB (GNU Debugger)" matching MCP servers:

Enables AI clients to search live documentation for popular developer libraries and return clean, source-cited summaries via a RAG pipeline.

Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.

Enables centralized access to Firecrawl, Exa, Qdrant, embeddings, Playwright browser automation, and MCP clients through a single token-protected HTTP and MCP interface.

Enables querying a hybrid-retrieval RAG pipeline (dense + BM25) over ingested PDF documents, returning answers generated by Gemini.

Enables AI-powered research by breaking a topic into subtopics, gathering information via agents, and compiling a report. Integrates with LangGraph and RAG for orchestration and contextual retrieval.

Provides persistent memory for AI agents using hybrid search (vector embeddings + BM25) with neural reranking, enabling storage and retrieval of insights, debugging solutions, and patterns across coding sessions.

An interactive learning assistant that helps developers understand AI-generated code changes through quizzes and blocking learning sessions. It tracks and searches debugging experiences using RAG to ensure users build long-term technical understanding rather than just copy-pasting solutions.

Enables debugging by analyzing bug reports, tracebacks, or code snippets to retrieve similar historical fixes and generate JSON answers with root cause, fix, and confidence.

A persistent memory server that stores and retrieves atomic coding insights like architectural decisions and debugging patterns for AI agents. It enables agents to maintain institutional knowledge across sessions using semantic search and local SQLite storage.

Enables control of Software Defined Radios and decoding of radio protocols through an AI-friendly Model Context Protocol interface, supporting RTL-SDR and HackRF hardware for signal analysis and protocol decoding.

A Model Context Protocol server that enables coding agents to search for and record software issue fixes with semantic search using natural language queries.

An autonomous AI software engineer and MCP server that uses LangGraph and a hybrid RAG index to autonomously edit, test, and debug codebases.

Enables semantic search over a software project's Markdown and text documentation by indexing document chunks in Qdrant and exposing retrieval through MCP tools.

Enables saving project facts as they occur and retrieving only task-relevant entries when starting new work, using Jev evaluation to rank them while preserving all stored context.

A local MCP server that gives AI assistants persistent long-term memory using a biological neural architecture with cortex, synapses, and hippocampus.

Enables querying Brazilian PCDT clinical protocols from Conitec and generating concise, context-specific treatment summaries using a local LLM, with verified literal citations and links to official PDFs.

Provides fast, token-efficient search over coding agent documentation (e.g., Claude Code, Cursor) using local SQLite FTS5 indexing, with tools for searching snippets, reading pages, and grepping markdown.

Crawl any website into clean Markdown, search through pages, read full content, and extract structured data using OpenAI, Claude, Gemini, or Grok — with auto-citation and resume support.

An MCP server that enables searching and retrieving ACL NLP conference papers from a Qdrant vector database using semantic search and structured filters like year, venue, and field of study.

Exposes hybrid retrieval (dense embeddings + BM25 + RRF) and document operations (search, fetch, rerank) as MCP tools, using Qdrant and OpenAI embeddings for local or server mode.