"Podcast Index" matching MCP servers:

Provides read-only MCP tools for AI agents and coding assistants to search podcast transcripts and episode metadata, retrieve transcript excerpts, and generate citations. It requires no login or API key.

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.

stdio server to read markdown with images or index markdown headings from a file, folder or URL

ake any website queryable by AI agents. Index a site then ask questions and get cited answers grounded in its content via RAG. Two tools: list_sites and ask_site.

Enables AI agents to scrape, crawl, clean, chunk, and index website content into Azure AI Search for full-text retrieval.

Exposes a RAG document-search API as MCP tools (rag_health, rag_ingest, rag_query), enabling agents to index and search markdown documents with cited results through natural language.

Enables AI agents to locally index and read-only search the BYR BBS, including full-text post search, thread reading, board browsing, and index status checks.

Enables AI assistants to search and retrieve transcripts from the Entra.Chat podcast about Microsoft Entra ID, with timestamped YouTube links and guest information.

Enables natural-language semantic search of a project's code through a local vector index, so questions that plain grep cannot match return ranked file:line references. It also indexes or refreshes projects on demand, reports index status, and optionally flags near-identical code across files.

Provides read-only tools to search and retrieve timestamped transcript evidence from the Pinkbike Podcast, enabling bike research questions to be answered with cited source passages.

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

Enables querying a private local-first archive of Founders podcast transcripts using hybrid full-text and semantic retrieval via MCP.

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.

Connects to Elasticsearch clusters through the Model Context Protocol, enabling natural language querying and management of Elasticsearch data. Provides tools to search indices, list available indices, and retrieve index mappings.

Enables local-first context retrieval and management from JSONL and Markdown sources through MCP, with a rebuildable full-text search index and support for stable reference pagination.

Enables AI assistants to interact with Meilisearch via the Model Context Protocol, allowing comprehensive index, document, and search management through a standardized interface.

Enables AI assistants to interact with Meilisearch through a standardized interface, supporting index and document management, search capabilities, settings configuration, task monitoring, and experimental vector search.

Enables AI agents to index, search, and validate lessons learned across project repositories via a local SQLite index, providing learning-loop resources and verified-solution tracking without loading full files into conversations.

Enables searching and managing a documentation index, including searching libraries, resolving versions, indexing from URLs or local files, refreshing content, and removing entries.

Enables AI agents to query Coveo's unified index for search, passage retrieval, and generative question answering, supporting multiple transport modes for client compatibility.