Media Context MCP

A local MCP server that lets text-only coding models read images, screenshots, PDFs, presentations, spreadsheets and other non-plain-text files. Built for OpenCode first (e.g. DeepSeek V4 Flash, which cannot see images), but client-agnostic: it works with any MCP-compatible agent (Claude Code, Codex, Antigravity, ...).

One tool: analyze_media — give it a file path and a focused question, get back concise, structured, evidence-backed Markdown.

Media file → detection → routing → MarkItDown / PyMuPDF / OCR / vision model
          → structured Markdown + typed evidence → content-addressed cache

What it is (and is not)

• MarkItDown is not a vision model. It converts document structure (DOCX/PPTX/XLSX/HTML/...) to Markdown.

• OCR extracts characters. It is the preferred source for exact text — error messages, code, identifiers — and knows nothing about layout or state.

• A vision-language model provides visual-semantic understanding — layout, relationships, UI state, charts, diagrams — and may hallucinate exact strings.

• This server combines all three and labels which is which in every result, so a calling agent knows what to trust.

• Cloud vision sends your media off-machine and is therefore off by default (MEDIA_MCP_ALLOW_CLOUD_VISION=false). Document extraction and OCR are fully local.

• A multimodal model can read a small one-off image directly; this MCP is mandatory mainly for text-only models, and useful for anyone as a cache/extraction layer for large or frequently reused media.

Related MCP server: Atlas Vision MCP

Requirements

• Python 3.11+

• Optional: Tesseract for local OCR

  • Windows: winget install UB-Mannheim.TesseractOCR

  • macOS: brew install tesseract tesseract-lang

  • Debian/Ubuntu: apt install tesseract-ocr tesseract-ocr-vie

• Optional: an OpenAI-compatible vision endpoint (Hugging Face router, OpenAI, OpenRouter, a local vLLM/Ollama shim, ...) for visual-semantic analysis

Install

git clone <this-repo> && cd media-context-mcp
uv venv && uv pip install -e ".[ocr]"     # or: pip install -e ".[ocr]"

Verify:

media-context-mcp doctor            # installation + configuration checks
media-context-mcp doctor --vision   # detailed vision checks (no network)
media-context-mcp doctor --vision --network  # one tiny real provider call

Configure

Copy .env.example and set at minimum the workspace sandbox:

MEDIA_MCP_ALLOWED_ROOTS=D:\Work\my-project        # ';'-separated on Windows, ':' elsewhere

Vision via Hugging Face Inference Providers (example preset)

MEDIA_MCP_VISION_PROVIDER=huggingface     # presets base URL https://router.huggingface.co/v1
MEDIA_MCP_VISION_API_KEY=hf_...           # token with inference permissions
MEDIA_MCP_VISION_MODEL=Qwen/Qwen2.5-VL-7B-Instruct   # example; verify current availability
MEDIA_MCP_ALLOW_CLOUD_VISION=true         # explicit opt-in: images leave your machine

Notes on Hugging Face: it is an initial configurable provider, not a permanent dependency — any OpenAI-compatible endpoint works via MEDIA_MCP_VISION_BASE_URL. Free/promotional model availability changes; credits are trial capacity, not unlimited inference. The server never silently switches models — fallbacks must be listed explicitly in MEDIA_MCP_VISION_FALLBACK_MODELS. A local VLM is a planned future provider.

OpenCode integration

Add to opencode.json in your project root (or the global config). Format verified against the OpenCode MCP docs:

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "media-context": {
      "type": "local",
      "command": [
        "C:\\path\\to\\media-context-mcp\\.venv\\Scripts\\python.exe",
        "-m", "media_context_mcp"
      ],
      "enabled": true,
      "environment": {
        "MEDIA_MCP_ALLOWED_ROOTS": "C:\\path\\to\\your\\workspace",
        "MEDIA_MCP_VISION_PROVIDER": "huggingface",
        "MEDIA_MCP_VISION_API_KEY": "{env:HF_TOKEN}",
        "MEDIA_MCP_VISION_MODEL": "Qwen/Qwen2.5-VL-7B-Instruct",
        "MEDIA_MCP_ALLOW_CLOUD_VISION": "true"
      }
    }
  }
}

Then restart OpenCode (or run /mcp to check connection status). Add an instruction like the one in AGENTS.example.md to your project's AGENTS.md so the model actually uses the tool.

Status: the configuration format above matches OpenCode's current docs; see docs/IMPLEMENTATION_REPORT.md for exactly what has and has not been verified end-to-end in this environment.

Example calls

// exact error text from a screenshot (local OCR, no cloud)
{ "path": "shots/build-fail.png", "mode": "ocr",
  "question": "Read the exact error message." }

// visual reading of a UI (needs vision provider + cloud opt-in)
{ "path": "shots/modal.png",
  "question": "Which component is misaligned relative to the design?" }

// pages 2-3 of a PDF, compact
{ "path": "docs/spec.pdf", "pages": "2-3", "detail": "compact",
  "question": "What are the deployment requirements?" }

CLI

media-context-mcp doctor [--vision] [--network]
media-context-mcp inspect <path> [-q QUESTION] [--mode ...] [--pages ...] [--json]
media-context-mcp show-config      # secrets redacted
media-context-mcp clear-cache
media-context-mcp benchmark [--live]

inspect runs the identical pipeline outside MCP — use it to separate media processing failures from MCP transport failures.

Security model (summary)

• File access is restricted to MEDIA_MCP_ALLOWED_ROOTS (canonical-path ancestry checks, symlinks resolved first; UNC paths, URLs and non-regular files refused).

• File-size, pixel-count, page-count, output-size and processing-time limits.

• Remote URL fetching is disabled; archives are refused (decompression bombs).

• Media content is treated as untrusted: instructions inside screenshots or documents are analysed as content, never followed.

• Cloud vision requires explicit opt-in; OCR-text forwarding is separately gated (MEDIA_MCP_SEND_OCR_TO_CLOUD). Screenshots may contain secrets or proprietary code — opt in deliberately. No automatic redaction is claimed.

• API keys never appear in logs, cache files, or tool output.

Details: docs/SECURITY.md. Architecture: docs/ARCHITECTURE.md. Troubleshooting: docs/TROUBLESHOOTING.md.

Tests

uv pip install -e ".[dev]"
pytest                      # offline, no credentials, fakes for OCR/vision
pytest -m vision_live       # optional real-provider smoke test (needs env vars)

License

MIT.