"Microsoft 365 MCP Server by Softeria AS" matching MCP servers:

A Python-based MCP server that enables AI assistants to search for infographics and data visualizations using the Serper API. It provides specialized tools for finding statistical graphics with optimized filters for size, aspect ratio, and specific curated sources.

Enables text-only language models to understand images by forwarding image and prompt requests to vision model backends via MCP, returning descriptive text. Supports multiple OpenAI-compatible providers with task routing and safety checks.

Enables MCP clients like Claude to analyze public YouTube videos by asking questions grounded in both audio and visuals through the Google Gemini API.

Reads passports, ID cards and driver's licences from a photo or scan and returns the printed fields and MRZ as structured JSON. One credit ($0.01) per recognised document; nothing is charged when no document is found.

MCP server that gives AI chat real vision by reading images, files, logs, and zip archives via local OCR in any language, with privacy-first processing.

Gives text-only LLM coding agents vision by routing images to a multimodal model and returning detailed textual descriptions. Supports local files, URLs, clipboard, base64, raw bytes, and multiple providers like OpenAI, Anthropic, and Gemini.

Enables AI agents to analyze public Reels, TikTok, and YouTube Shorts by extracting timestamped transcripts, visual understanding, and searchable video maps, then query specific moments via MCP tools.

Exposes OpenAI's gpt-image-2 and gpt-image-1.5 to Claude Code as a single tool for generating, editing, or composing images with automatic model selection.

This MCP server gives pure-text LLM agents vision capabilities by integrating the free GLM-4.6V-Flash visual model, enabling image, video, and file understanding through standard MCP tools.

Provides vision capabilities to text-only LLMs by acting as a cloud vision adapter layer, enabling image understanding and OCR text extraction through a single MCP tool.

A local, read-only MCP server that lets AI agents inspect and analyze photo libraries by scanning files, aggregating EXIF stats, finding duplicates, scoring blur, and generating cull reports without uploading any data.

Enables drawing commands to be rendered into raster images, supporting shapes like lines, rectangles, circles, ellipses, and triangles, with results returned as inline PNGs or saved to files.

Provides image recognition capabilities to Codex/Claude Code by routing 'look at screen/screenshot' requests to a multimodal model via an API relay. Enables UI automation agents to analyze local screenshots and return descriptions or structured JSON coordinates.

Enables AI video dubbing by letting users submit video URLs for dubbing into Russian, English, or Spanish while preserving original speakers' voices through voice cloning, plus job status and cost estimation tools.

Enables agents to inspect local videos, audio recordings, and documents by producing storyboard grids, timestamped transcripts, page images, and page-aligned text, with tools for probing media, zooming into details, locating terms, and reading subtitles. It works only with local absolute paths and does not download content or call an LLM.

Exposes OpenAI's gpt-image-2 (image generation and editing) as an MCP server for tools like generate_image, edit_image, and iterative edit sessions.

Enables text-only reasoning models to see images by wrapping vision-language models as MCP tools, supporting image description, OCR, chart analysis, and custom questioning within MCP-compatible IDEs.

A Node.js MCP server that enables image understanding by calling the MIMO multimodal model, supporting local file paths and URLs.

Lets coding agents such as Claude Code and Codex hand summarising, field extraction, classification and OCR to the free, offline on-device model for Apple Foundation Models on macOS 27. One Rust binary, installed with Homebrew.

Captions video and audio files on your computer with Cuecaps: animated word-by-word captions in 10 styles, exported as a burned-in MP4, a transparent MOV/WebM overlay for Premiere, Resolve or CapCut, or SRT/VTT subtitles. Rendering runs in local headless Chrome, so the video never uploads; captioning needs a Cuecaps Creator or Pro key.

Give text-only LLM agents eyes by describing images through a vision-language model via MCP or CLI.

MCP server for the Zerochan anime image board API. Browse the global feed, search by tags, and retrieve full entry metadata — directly from any MCP-compatible AI assistant.

Zero-dependency MCP server that equips text-only LLMs (e.g., DeepSeek) with vision by converting images to text descriptions via OpenAI-compatible vision APIs, exposing an img2text tool with multi-provider fallback.

An MCP server that adds vision capability to any LLM by forwarding images to OpenRouter vision models, returning text analysis. Supports image analysis, model listing, and config diagnostics.

Enables text-first agents to inspect images via focused questions, returning compact, checked evidence packets. Reduces visual context by ~90% while preserving expected fields.

An MCP server that enables AI assistants to search royalty-free stock photos using natural language, example images, or similarity, and render results as an inline thumbnail grid.

Author, validate, preview, and render motion-graphics videos from a single JSON document (Clipkit Protocol). 16 tools with in-chat PNG previews, captions, templated promos, and hand-off to a free browser editor by link.

Enables inspecting, creating and modifying MakeHuman characters inside Blender — phenotypes, rigs, assets, skins, faces, expressions and saved presets — through 34 named tools served by a companion Blender bridge add-on that dispatches a closed command vocabulary instead of executing code. It also exposes read-only introspection, catalogue and documentation lookups that always answer, even when Blender is unreachable.

Enables vision LLMs to read PDFs by automatically detecting text corruption and switching between text extraction and image rendering modes, while preserving reading order and filtering unnecessary images to prevent token overflow.

Lets a text-only LLM read images and PDFs by delegating to a vision model running on your own GPU.