"Connecting Firebase to a data source or application" matching MCP servers:

A local-first, open-source MCP server that analyzes jobs, matches your CV, tailors documents, and tracks applications — all on your machine with no data uploaded.

MCP server for summarizing content from Baidu Netdisk, local files, Bilibili, X, and web articles, producing Obsidian notes with bidirectional links and Feishu digests.

MCP server that converts PDF, video, web, and audio inputs into structured Markdown notes with support for checkpointing, batch processing, and Obsidian integration.

A Model Context Protocol (MCP) server that automates generating LinkedIn post drafts from YouTube videos. This server provides high-quality, editable content drafts based on YouTube video transcripts.

Fetches category data from a Q&A service API and returns a markdown summary grouped by category.

Enables LLM clients to search a firm's precedent vault and draft grounded contract clauses, legal memos, and document summaries.

An HTTP MCP server that indexes large documents into exact-line-numbered sections, enabling AI models to locate, read, summarize, and edit parts of a document without ingesting the whole file.

Enables interactive AI chat through the Anthropic API, with document retrieval, slash commands, and extensible MCP tool integrations.

Enables AI language models to interact with YouTube content through a standardized interface, providing tools for retrieving video information, transcripts, channel analytics, and trend analysis.

Enables local analysis of unstructured documents (PDF, DOCX, PPTX, SVG, PNG) by extracting text and structure with citation anchors, and verifies summaries against source material before a human approves saving a report.

A small MCP server that gives agents rich context about a YouTube video — its transcript, jump-to-the-moment deep links, metadata, and most-replayed moments — so they can answer questions, summarize, pull quotes, or surface highlights.

tooltrim reduces the tokens agents spend re-reading bloated tool results. Run it as an MCP server exposing compress and expand_tool_output, or as a gateway in front of any upstream MCP server: it re-exposes the upstream tools unchanged and shrinks each result (HTML/JSON/logs/tables) before it reaches the model, keeping the relevant content only.

Measures AI-detection risk in text and provides a line-by-line rewrite plan to lower the score.

Self-hosted digests of your Telegram channels over MCP: AI summaries, the last digest and raw per-channel messages. Works with OpenAI, Anthropic or local Ollama.

Two self-hosted MCP servers: manage a local model machine (Ollama pull/switch, LoRA training) and bridge to local Ollama/vLLM for pure language processing tasks (writing, summarizing, classifying, extraction) without giving the calling agent tools or file access.

Lets any AI agent score and simplify its own text before it reaches a human, using Flesch readability metrics and plain-language rewrites entirely on the local machine.

A FastMCP server that enables AI assistants to extract structured information from unstructured text using Google's langextract library through a secure, optimized Model Context Protocol interface.

Enables LLMs to interact with Fathom.video API for managing meeting recordings, retrieving transcripts and AI-generated summaries, searching meeting content, and accessing analytics and team data.

Enables natural language interaction with local .docx files, allowing users to find, read, search, and summarize Word documents using friendly names and location hints.

Provides MCP-compatible AI clients with offline text analysis and rewriting tools, including statistics, extractive summaries, keywords, readability scores, case conversion, entity extraction, and diffing, all running locally without API keys or network calls.