Spec Driven Development
by formulahendry
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Runs on your computer★ 440MITUpdated 14 months ago
Install
videoctx
Overview
Description from the maker (original English)
Read videos like images: index once locally, query the text layer, fetch frames on demand.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiNot measured yet★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Other
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- The registry lists no key to fill in.
You need
- PyPIPython’s uv installed (for the uvx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"videoctx": {
"command": "uvx",
"args": [
"videoctx",
"mcp"
]
}
}
}Run this in your terminal
claude mcp add videoctx -- uvx videoctx mcpOr copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"videoctx": {
"command": "uvx",
"args": [
"videoctx",
"mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"videoctx": {
"type": "stdio",
"command": "uvx",
"args": [
"videoctx",
"mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account dunfred, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Otheri
- More stars than 0% of the other 32505 servers in this category
- Published to the registry more recently than 71% of the other 32505 servers in this category
- Basic checks equal to or better than 100% of the other 32505 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is videoctx safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (0.1.5) reached the registry 15 days ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
What can I use instead?
In the same category: Spec Driven Development, Remote Desktop Commander, topo^alpin
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Remote Desktop Commander
by desktop-commander
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Runs in the cloud★ 211Updated yesterday
Install
topo^alpin
by topoalpin.app
Avalanche bulletins and mountain weather forecasts for any place in the Alps.
Runs in the cloudUpdated today
Install
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
videoctx
Index a video once. Query it cheaply, forever, with any LLM.
videoctx ("video context") turns any video into an open, portable Video Context index (.vctx, see SPEC.md) using only local, model-free tools: ffmpeg keyframes, PySceneDetect shot boundaries, faster-whisper transcripts, and optional one-line visual captions from any cheap VLM. An agent answers most questions from the index's text layer for fractions of a cent, and pulls individual frames on demand only when a question needs pixels.
videoctx index lecture.mp4 # one-time, local, freevideoctx show lecture.vctx # the text view the model reads videoctx ask lecture.vctx "What diagram is shown when she explains backprop?"
What people use it for
Ask questions about videos the way you'd ask about a document:
- Lectures and courses: "Summarize this 50-minute lecture." "Where does she explain backpropagation, and what's on the slide at that moment?"
- Meetings and interviews: "List the action items with timestamps." "Find the moment the client pushed back on pricing."
- Screen recordings: "At what time does the error appear, and what does the dialog say?"
- Content creation: "Find the three strongest moments for short clips." "Generate chapter markers."
- Video archives: index a folder of talks once, then every future question across all of them costs pennies and answers in seconds.
- AI agents: give any MCP-capable agent (Claude Code, Cursor, ...) the ability to read video at all.
Who this is for: developers and anyone comfortable running a few terminal commands. You'll need Python, ffmpeg, and an Anthropic API key for question-answering (indexing itself is free and offline). If that's not you yet, star/watch the repo: a no-install version is on the roadmap.
Why
Native video ingestion re-tokenizes the whole video on every question: an hour of footage is ~250-350k tokens, every single time. Proprietary video platforms index into closed representations behind per-minute pricing. videoctx is the third option: an open on-disk format any model can consume, produced by free tools, where the marginal cost of a follow-up question on an hour-long video is about $0.02.
Benchmark results (Video-MME, Oct 2026)
Full protocol and honest cost accounting (including prompt-cache write premiums) in paper/draft.md. Query model: Claude Opus 4.8.
Numbers below are single-touch runs of the frozen configs (v0.2: captions + OCR + evidence rules, Opus 4.8 loop; v0.3, 2026-10-21: adds text-evidence rules, verbatim transcript quotes and a final letter-vs-evidence check) under full cost accounting. Where we measured the same configuration more than once, we report the observed range: on n=150, single runs wobble by 2-3 questions; on n=30, by 2-3 questions (= 7-10 points). Treat sub-3-point deltas on this benchmark as noise.
Subset composition caveat: videos were taken in Video-MME dataset order, which clusters by domain. The medium and long cells are entirely Knowledge-domain videos; the short cells are 76-88% Knowledge (remainder Film & Television). Read these as Knowledge-dominated subsets of Video-MME, not the full benchmark; future splits will be domain-stratified.
Short videos, held-out test half (n=150):
| Method | Accuracy | $/question |
|---|---|---|
| videoctx v0.4 | 84.7% | $0.025 |
| videoctx v0.3 | 82.0% | $0.025 |
| videoctx v0.2 | 80.7% (equivalent configs ranged 80.7-85.3%) | $0.025 |
| Uniform frame sampling | 74.0% | $0.0172 |
| Gemini Flash-Lite native (pilot 30) | 70.0% | $0.0009 |
| Gemini Flash native (pilot 30) | 53.3% | $0.0047 |
Medium videos, 4-15 min (n=30, wide error bars):
| Method | Accuracy | $/question |
|---|---|---|
| Uniform frame sampling | 80.0% | $0.081 |
| videoctx v0.7 (contact sheet 60@512 in cached prefix) | 70.0% | $0.092 first-touch, ~$0.02 follow-up |
| videoctx v0.2 through v0.6 | 66.7% (near-identical failure set) | $0.051-0.062 |
| Gemini Flash-Lite native | 70.0% | $0.0034 |
Medium is our weakest cell and remains the named target: naive leads on raw accuracy. Ten questions failed identically across five configs (prompt rules, a text-view bug fix, dense captions, a 24-thumb contact sheet); the sixth config, a 60-thumbnail 512px contact sheet baked into the cached prefix, recovered one. The receipts tell a consistent story: these videos are fast-cut and fully captioned, and the remaining gap is visual detail bandwidth at question time, naive's one structural advantage. We publish the ladder rather than only its best rung.
Correction (2026-10-21): a text-view bug had been dropping the header line (duration, shot count) on every OCR-enriched index since OCR shipped; all v0.2/v0.3 numbers were measured under that bug. v0.4 is the corrected configuration (short +2.7 points, long back to 80.0%, medium unchanged).
Long videos, 30-60 minutes (n=30):
| Method | Accuracy | $/question | Follow-up $/question |
|---|---|---|---|
| videoctx v0.5 (dense captions) | 83.3% | $0.109 cold (measured $0.047, see note) | $0.021 |
| videoctx v0.4 | 80.0% | $0.099 | $0.019 |
| videoctx v0.2 / v0.3 | 80.0% / 76.7% (n=30 noise band) | $0.098 | $0.019 |
Cost correction (2026-10-22): the v0.5 long run started 57 minutes after the v0.4 run, and for the 7 of 10 videos whose text views were unchanged by dense captioning, it silently inherited v0.4's still-live 1-hour prompt caches, flattering first-question costs. $0.109/q is the cold-equivalent (first question $0.285, follow-ups $0.021 as measured). Benchmark runs are now cache-salted per config so this cannot recur. | Uniform frame sampling (60-frame cap) | 70.0% | $0.133 | $0.133 | | Gemini Flash-Lite native | 76.7% | $0.022 | $0.022 (5-9 min latency/q) | | Native ingestion (published Pro rates, est.) | pending | ~$0.50 | ~$0.50 |
One-time index cost: ~$0.10-0.25 per hour of video (captions + local OCR + local embeddings), pennies otherwise.
*Cost correction history: early short-video figures ($0.0067/q) undercounted prompt-cache write premiums; the metering was fixed 2026-10-18 and all figures above are fully loaded. Details in the paper's Section 7.3.*
Install
git clone <repo> && cd videoctxuv venv && uv pip install -e .
Requires ffmpeg on PATH. videoctx index and the MCP server's core tools run fully locally with no API key. videoctx ask uses the Anthropic API (ANTHROPIC_API_KEY or an ant auth login profile).
MCP server: let any agent read videos
claude mcp add videoctx -- $(pwd)/.venv/bin/videoctx-mcpThe calling agent's own model reads the text layer and frames directly; the server makes no model calls and needs no key. Tools: video_overview (cheap text layer), survey_video (one thumbnail per shot), get_frames(path, t0, t1, detail), transcript_search, find_moments (semantic visual search, needs videoctx embed once per index), index_video. The server's instructions carry the evidence rule: visual answers must cite fetched frames, never general knowledge.
Reproduce the benchmark
uv pip install -e ".[eval]"python eval/prepare_videomme.py --duration short --videos 100 --out eval/data python eval/split_dev_test.py eval/data/videomme_short.jsonl python eval/run.py eval/data/videomme_short_test.jsonl --methods videoctx,naive
eval/methods.py documents each baseline. Cost accounting counts cache writes at their real premium (1.25x / 2x by TTL); see the paper's Section 7.3 for the accounting traps this avoids.
Format
The .vctx directory is plain JSON + JPEG: manifest.json (provenance, parameters, source hash), index.json (shots, keyframes, transcript, captions), frames/. Consumers must ignore unknown keys; enrichment layers (OCR, embeddings) are reserved in SPEC.md v0.1.
Status and roadmap
Working and benchmarked: indexing, query loop, MCP server, eval harness. Next: medium-duration subset, Pro-tier native baseline, cross-model portability runs (same indexes via non-Claude models), OCR and per-shot embedding layers, spec v1.0.
License
Apache-2.0. Copyright 2026 Fred Dunyo.