"Accessing the OpenAI API" matching MCP servers:

Enables sentiment analysis of text blocks using the Api Ninjas API, returning sentiment scores and overall sentiment classification for up to 2000 characters of text.

A TypeScript-based MCP server that implements a simple notes system, providing resources for accessing notes via URIs, tools for creating notes, and prompts for generating summaries.

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.

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.

Search and discover YouTube channels via natural language, track them, and summarize videos without transcripts using Gemini API.

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.

MCP server for koreafilings.com — AI-summarized Korean DART (전자공시) corporate disclosures, paid per call in USDC via the x402 protocol on Base. Tools: get_pricing (free), get_disclosure_summary (0.005 USDC).

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.

MCP server that searches the web, reads pages, and returns concise summaries with cited sources via the research_web and read_url tools.

Allows AI agents to fetch, search, and summarize YouTube video transcripts without an API key, including chapter summaries.

Model Context Protocol server exposing the bankstatementparser library as first-class agent tools for reading bank statements.

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.

Exposes the Aporix document optimization API as an MCP tool for AI agents, enabling document compression and token/cost savings analysis.

Keyword Extractor AI - MCP server providing AI-powered tools and automation by MEOK AI Labs

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.

Enables AI agents and MCP clients to browse Roomtone meeting notes, add YouTube or pasted-text sources, generate summaries, ask questions answered from transcripts, and retrieve raw transcript text. Recording itself stays in the Roomtone app, so the server only reads and adds to existing meetings.

Turns plain text or Markdown, web pages, and local PDFs into editable Markdown outlines that can be rendered as standalone, offline-capable interactive HTML mind maps. Works with any MCP client with no API key required, since the connected model writes the final outline from a draft and cleaned source text.

Unofficial MCP server for working with Kagi without API access (you'll need to be a customer, tho). Searches and summarizes. Uses Kagi session token for easy authentication.

Enables AI agents to transcribe, translate, and summarize audio via Zoom AI Services APIs, providing an 'ear' through tools like transcribe_audio, summarize_transcript, and translate_text.

MCP Long Context Reader is a Python-based toolkit designed to overcome the context window limitations and high costs associated with Large Language Models (LLMs) processing extensive documents. It provides a FastMCP server with multiple, powerful strategies for an LLM agent to 'read' and query long documents without needing to load the entire text into its context window.

An open-source MCP server for querying the Tenarius RO wiki and Ragnarok Online Pre-Renewal references. It provides structured search and summaries with source metadata indicating server and era applicability.

Enables an assistant to transcribe video or podcast recordings, then read, search, summarize, and manage the resulting transcripts through MCP tools.

Enables agents to check and retrieve concept-structured YouTube video summaries from the oratilo library via lookup and full-text search tools, avoiding redundant summarization work.

Generates natural, fluent Chinese content that reads like human writing using the Qwen3-Next-80B-A3B model. Supports web search for latest information and direct file saving with customizable writing rules.

Delegates replaceable grunt work (boilerplate, formatting, translation, long-document summarizing) from a premium agent to cheap models behind a local LiteLLM proxy, auto-routing each task by type. Delegated calls run in a separate process, so the subscription session and the API credentials never share an environment.

Enables AI assistants to transcribe links (YouTube, Google Drive, Dropbox), list and search your recordings for spoken words and moments, and read transcripts in json/txt/srt/vtt, summaries and metadata. It also lets them request new summaries and delete recordings, all through your own account with API-key or OAuth sign-in.

An MCP implementation that integrates the Brave Search API, providing comprehensive search capabilities including web, local business, image, video, news searches, and AI-powered summarization.

Enables agents to parse resumes and job postings into structured OTP/OJP skeletons, validate them against JSON Schema, and extract agent-friendly summaries, all without LLM calls.