io.github.tlq5l/agentic-video
PYPI · AGENTIC-VIDEO
Local video tools for AI agents: contact sheets, transcripts, cuts, loudness and sync checks.
Available components
Last scanned (8 hours ago)
Scores higher than 75% of PyPI servers
Category Design and Media
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs hatchling.build at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
- 2 of 29 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency97
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Cryptographically verified build provenance (signed, bound to tlq5l/agentic-video). View diagnostics → Pass
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 3 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability58
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 11897 tokens (~475/item across 25 items; 25 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management17
- Stability observed for 5 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage98
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 93% of tool parameters carry a description.Partial
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 25 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 25 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities20
- Spec-recency check failed: implements MCP spec 2024-11-05; the latest is 2026-07-28. See how to fix → Fail
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 5 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 13 to 17. That category is still filling its 30-day observation window: 4 days of observed history at the previous scan, 5 at this one. The score rises as the window fills, whether or not the server changes.
- 3 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 7 to 10. That category is still filling its 30-day observation window: 2 days of observed history at the previous scan, 3 at this one. The score rises as the window fills, whether or not the server changes.
- 1 Nov 26 +16
- Tool safety: pass → unverified ▼ security
- Malware scan: unverified → pass ▲ security
- Stability: Stability not yet verified: we do not have a sandbox capture of the MCP schema this version of the package serves yet. security
- Capabilities: fail → unverified ▼ functional
- Tool coverage: 100 → unverified ▼ functional
- Stability: unverified → 0.03 ▲ functional
- First check of Schema quality: unverified functional
- Package version: 0.3.3 → 0.3.4 functional
- 31 Oct 26 59
First indexed and scored.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Analysed pypi/agentic-video@0.3.4
Provenance Verified
A signed build attestation was found and verified, binding this exact artifact to the source repository it claims to come from.
| Result | Verified |
|---|---|
| Ecosystem | pypi |
| Reason | Verified |
| Discovered via | Registry attestation endpoint |
| Source repo | tlq5l/agentic-video |
| Certificate issuer | https://token.actions.githubusercontent.com |
| Certificate SAN | https://github.com/tlq5l/agentic-video/.github/workflows/publish.yml@refs/tags/v0.3.4 |
| Rekor log index | 3625917787 |
| Predicate type | PyPI publish attestation https://docs.pypi.org/attestations/publish/v1 |
| Subject digest | sha256:03d3bcae49d5ee20178039cff910853b6488e6bf95bb7936db753489a57db467 |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 29 packages
| Packages resolved | 29 |
|---|---|
| Stale | 1 |
| No linked repository | 1 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
watch_for ~764
Watch a stretch of a video for yes/no conditions with a cloud model (Clef on Cloudflare Workers AI) and return the moments each one started to hold. One frame every interval_sec (default 5 s, 1 to 600) from [start_sec, end_sec] is asked every condition (1 to 8 yes/no questions about ONE frame, e.g. "Is a person visible?"); a condition wakes at the sample where its chance of yes has been at least cut (default 0.5) on `consecutive` samples in a row (default 2), at most once per cooldown_sec (default 30) per condition. THIS SENDS VIDEO FRAMES TO CLOUDFLARE: never use it on personal recordings. The server needs CLOUDFLARE_ACCOUNT_ID and CLOUDFLARE_API_TOKEN (or AGENTIC_VIDEO_CF_TOKEN_COMMAND, a command that prints the token) and the call fails naming what is missing, before anything is sent. Only the questions you write can wake: a condition nobody asked about is never reported, so list every event you care about. An event must last at least consecutive x interval_sec to be caught (two samples 5 s apart need about 10 s); lower interval_sec or consecutive for shorter ones, which costs more calls. Accuracy on real footage is not yet measured. At most 720 samples per call; the call answers within about 50 s even when Cloudflare is slow: a longer range, or a sample that keeps failing after its retry, returns the finished samples with complete=false and next_start_sec to continue from (the cooldown does not carry over). This is for finding when something starts to be true; to inspect what happened use inspect_window, for scene changes use detect_cuts, and for sounds use audio_onsets. Results are not cached.
| Name | Type | Req | Description |
|---|---|---|---|
| conditions | array | yes | 1 to 8 distinct yes/no questions about one frame, e.g. ["Is a person visible?", "Is a red rectangle visible?"]. Name what to look for; vague questions give vague chances. |
| consecutive | integer | – | Samples in a row that must count as yes before a wake (1 to 5, default 2). |
| cooldown_sec | number|string | – | Seconds after a wake during which the same condition wakes no more (0 to 3600, default 30). Also accepts a timecode string in MM:SS.mmm form, e.g. 00:14.250. |
| cut | number | – | Chance of yes (above 0, up to 1) at which a sample counts as yes (default 0.5). |
| end_sec | number|string | – | End of the range in seconds (default: the end of the video). Also accepts a timecode string in MM:SS.mmm form, e.g. 00:14.250. |
| interval_sec | number|string | – | Seconds between samples (default 5, from 1 to 600). The range may hold at most 720 samples. Also accepts a timecode string in MM:SS.mmm form, e.g. 00:14.250. |
| source | string | yes | Local video file path, or a video URL (X/Twitter post, YouTube link, other sites yt-dlp supports, direct .mp4 link). A URL is downloaded once and cached; while it downloads the result is status=in_pr… |
| start_sec | number|string | – | Start of the range in seconds (default 0). Also accepts a timecode string in MM:SS.mmm form, e.g. 00:14.250. |
| Name | Type | Req | Description |
|---|---|---|---|
| complete | boolean | – | – |
| conditions | array | – | – |
| consecutive | integer | – | – |
| cooldown_sec | number | – | – |
| cut | number | – | – |
| input_tokens | integer | – | – |
| interval_sec | number | – | – |
| local_path | string | – | – |
| model | string | – | – |
| next_start_sec | number|null | – | Where to continue when complete is false; null when complete. |
| note | string | – | Plain-English remark about the call: a capped argument, omitted items, or an ignored unknown argument. |
| retry_after_seconds | number | – | – |
| samples | integer | – | – |
| scanned_to_sec | number | – | Time of the last sample looked at. |
| source_path | string | – | – |
| source_url | string | – | – |
| start_sec | number | – | – |
| status | string | – | – |
| wakes | array | – | – |
No examples provided.
How do I install the io.github.tlq5l/agentic-video MCP server?
io.github.tlq5l/agentic-video runs locally as a PyPI package, launched with uvx agentic-video. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · agentic-video
claude mcp add tlq5l-agentic-video -- uvx agentic-video
What is the io.github.tlq5l/agentic-video MCP server?
io.github.tlq5l/agentic-video is an MCP server listed in the public MCP registry as io.github.tlq5l/agentic-video. Local video tools for AI agents: contact sheets, transcripts, cuts, loudness and sync checks. This page covers its PyPI package (agentic-video).
Is the io.github.tlq5l/agentic-video MCP server safe to use?
io.github.tlq5l/agentic-video scores 77 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 November 2026. Its build provenance is signed and verified. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the io.github.tlq5l/agentic-video MCP server expose?
io.github.tlq5l/agentic-video exposes 25 tools: count_repetitions, count_occurrences, probe_video, transcribe, search_transcript, and 20 more. Their descriptions and schemas cost roughly 11,897 tokens of context every time the server is loaded.
Is the io.github.tlq5l/agentic-video MCP server still maintained?
io.github.tlq5l/agentic-video is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the io.github.tlq5l/agentic-video MCP server under?
io.github.tlq5l/agentic-video declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.