"IBM MCP Context Forge - Model Context Protocol Framework" matching MCP servers:

Enables an agent to process local video files and then read, search and inspect the results: timestamped transcripts, on-screen text and key frames as images. Everything runs locally, so an agent can answer questions like how to perform a task shown in a screencast or meeting recording.

Give your AI assistant eyes and ears — analyze any video, audio, or image, entirely on your machine.

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.

Compress OCR-heavy PDFs into dense packed images so agents can work with long visual documents.

Enables Codex image generation and inspection workflows to run as durable local jobs while keeping provider media bytes outside the public MCP boundary and returning only bounded text handoffs, hashes, relative references, and Job IDs. It supports idempotent submission, restart recovery, an offline mock provider, and optional OpenAI-backed generation or inspection with artifacts written to configured workspace paths.

Enables Codex to run durable, context-bounded image generation and inspection jobs through local MCP tools, returning compact text results, hashes, relative references, and Job IDs instead of media bytes.

Enables text-only coding models to read images, PDFs, presentations, spreadsheets, and other non-text files through a single analyze_media tool, combining local document extraction, OCR, and optional vision models with clear evidence labeling.

Enables Codex to run context-bounded image generation and inspection as durable local jobs, returning bounded text handoffs, hashes, relative references, and Job IDs while keeping provider-returned media bytes outside the MCP boundary.

A durable local MCP server that runs image generation and inspection as persisted jobs, keeping provider-returned media bytes behind the MCP boundary while returning only bounded text handoffs, hashes, relative references, and Job IDs. This keeps image-heavy Codex workflows context-bounded, idempotent, and resumable, with offline mock or optional OpenAI providers.

Enables Codex to run durable, context-bounded image generation and inspection jobs that return only bounded text handoffs, hashes, relative references, and Job IDs instead of media bytes. Supports idempotent submission, restart reconciliation, and optional OpenAI-backed generation and inspection.

Runs image generation and inspection as durable, idempotent local jobs that Codex submits and polls by Job ID, keeping provider-returned media bytes behind the MCP boundary. Returns only bounded text handoffs, hashes, and relative artifact references so image-heavy Codex workflows stay responsive, resumable, and context-bounded.

Enables Codex to run image generation and inspection as durable, resumable local jobs whose provider-returned media bytes stay behind the MCP boundary. Callers receive only bounded text handoffs, hashes, relative references, and Job IDs instead of pixels, keeping image-heavy workflows context-bounded.

Enables Codex to run context-bounded image generation and inspection as durable local jobs, returning only bounded text handoffs, hashes, relative references, and job IDs while keeping media bytes behind the MCP boundary.

Enables Codex to run image generation and inspection as durable, idempotent local jobs that return only bounded text handoffs, hashes, relative references, and Job IDs while keeping provider media bytes behind the MCP boundary. This keeps image-heavy workflows context-bounded, restart-safe, and resumable across multiple concurrent Codex tasks.

Enables Codex to run image generation and inspection as durable, context-bounded local jobs, returning only bounded text handoffs, hashes, relative references, and Job IDs while keeping provider media bytes outside the public MCP boundary.

Runs image generation and inspection as durable, idempotent local jobs through bounded MCP tools, so Codex can create and analyze image-heavy assets without media bytes entering the task context. Returns only compact text handoffs, hashes, relative references, and Job IDs, with offline mock or opt-in OpenAI providers.

Enables Codex to run image generation and inspection as durable, resumable local jobs while keeping provider-returned media bytes outside the MCP boundary. Returns only bounded text handoffs, hashes, relative artifact references, and Job IDs so image-heavy workflows stay context-bounded.

Enables Codex to run context-bounded image generation and inspection as durable local jobs, returning only bounded text handoffs, hashes, relative references, and Job IDs while keeping media bytes outside the task context.

Enables Codex workflows to submit context-bounded image generation and inspection as durable local jobs, keeping provider-returned media bytes outside the MCP result boundary and returning only bounded text handoffs, hashes, relative references, and Job IDs.

Enables Codex to run image generation and inspection as durable, restart-safe local jobs while keeping provider-returned media behind the tool boundary, returning only bounded text handoffs, hashes, relative artifact references, and Job IDs. This keeps image-heavy workflows context-bounded, resumable, and free of image bytes or base64 data in the public results.