Interview Prep MCP
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- AlicenseAqualityBmaintenanceGives AI assistants a read-only, offline interface to the FinOps Foundation's published guidance — its principles, phases, domains, 22 capabilities with Crawl/Walk/Run maturity assessments, personas, and an 88-entry KPI library — so FinOps questions are answered with the Foundation's own text and formulas rather than improvised ones. It exposes 11 tools, document resources, and four guided prompts over stdio, with every response carrying its source URL and license.1175 npmMIT
- AlicenseNot gradedqualityBmaintenanceEnables any agent to query the same interview corpus as the UI over stdio, exposing tools to list transcripts, search quoted lines, retrieve verified themes with citations and dropped reasons, and fetch source lines.7 npmMIT
- AlicenseNot gradedqualityBmaintenanceProvides LLM clients with market intelligence tools over stdio, including market snapshots, price history, sentiment scoring, evidence search, and research briefs, with saved-run resource access.AGPL 3.0
- FlicenseNot gradedqualityAmaintenanceEnables AI agents and MCP hosts to run hybrid vector + BM25 retrieval over a locally indexed second brain of scattered notes, docs, PDFs, chat logs, and wiki pages, returning section-level citations. It exposes 8 tools plus resources and prompts so hosts like Claude Desktop, Cursor, Cline, and Claude Code can search and ask questions of your own files.-
- FlicenseNot gradedqualityCmaintenanceEnables MCP hosts like Cursor or Claude to query an enterprise knowledge base via stdio, providing RAG-based answers with cited sources.-
- AlicenseNot gradedqualityCmaintenanceEnables coding agents to search a locally indexed repository and get back a token-budgeted deck of ranked code and doc snippets instead of a full-tree grep dump, with lexical, semantic, and prose-answering modes plus index status reporting. Runs fully offline for lexical queries and optionally uses an OpenAI-compatible provider for semantic or ask modes.MIT
TDQS
Scored across 5 tools
Each tool has a reasonably distinct role: list_topics is an index, get_questions is filtered retrieval, get_answer_framework is single-question deep dive, random_drill is a timed mock, and search_questions is keyword lookup. The only real overlap is between get_questions and search_questions (both return question lists) and between random_drill and get_questions (both sample questions), but the descriptions make the intent differences clear.
Four of five names follow a clean verb_noun pattern (list_topics, get_questions, get_answer_framework, search_questions). random_drill breaks the pattern as an adjective+noun with no verb, though it is still readable and unambiguous.
Five tools is well-scoped for a question-bank server: discover, retrieve, drill, deep-dive, and search. Each tool earns its place with no redundancy padding the surface.
The core read lifecycle is covered: topic discovery, filtered retrieval, answer frameworks with rubrics, random drilling, and search. Minor gaps exist — no single-question fetch by id independent of the answer framework, and no attempt/progress tracking across drill sessions — but agents can work around both.