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vouh-chronos-mcp

vouh-chronos-mcp

by urielhgb7-arch
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

Extraire les frames horodatées

extract_frames

Splits a capture video into numbered JPEG frames and creates exact timestamped indexes (index.json, index.md) for precise frame review and analysis.

Instructions

Découpe la vidéo d'une capture en frames JPEG numérotées depuis 0 avec leur index horodaté exact (index.json, index.md).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "capture_id": "<capture_id>"
    },
    "name": "extract_frames"
  }
}

Input Schema

NameRequiredDescriptionDefault
fpsNoCadence d'extraction en i/s
roiNoRecadrage ROI [x, y, w, h] en pixels (§9.2)
sheetNoGénérer des planches contact (true pour 4×4, ou {cols, rows})
widthNoLargeur maximale des frames en px (≤ 1280)
strategyNoStratégie d'échantillonnage (§5.3, défaut : 'two_pass')two_pass
zip_nameNoNom de l'archive ZIP optionnelle (^[a-z0-9_-]{1,64}$)
capture_idYesIdentifiant de la capture
max_framesNoNombre maximal de frames à produire
deduplicateNoActiver le dé-doublonnage perceptuel dHash 64 bits (§5.8)
scene_thresholdNoSeuil de changement de scène (0–1)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are supplied, so the description carries the full behavioral burden. It does disclose concrete output artifacts (JPEG frames numbered from 0, plus index.json and index.md with exact timestamps), which the schema does not. However, it says nothing about persistence side effects (files written to disk), whether existing frames are overwritten, cost/runtime of video decoding, or how the sampling strategy and frame budget interact.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler, stating the operation and its outputs immediately. It is efficient, though it spends its only clause on output artifacts rather than on the decision criteria that a 10-parameter tool actually needs.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 10 parameters, no annotations, and no output schema, the description is thin for the tool's complexity. The most consequential choice, the sampling strategy enum with its two_pass default, plus the max_frames budget and deduplicate behavior, are never explained, leaving the agent to pick a strategy from the schema's one-line hints.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter is already documented in the schema, making 3 the baseline. The description adds no parameter-level meaning: the four sampling strategies (fixed_fps, scene, budget, two_pass), the fps/max_frames interplay, ROI cropping, or contact-sheet generation are all left to the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb and resource: it slices a capture's video into JPEG frames numbered from 0, with a timestamped index. That is far more concrete than the title alone and implies a distinct role from the read-oriented sibling get_frames. It does not explicitly contrast itself with siblings such as get_frames or analyze_motion, so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use or when-not guidance, no mention of prerequisites (does the capture need to exist first?), and no routing to alternatives such as get_frames for already-extracted frames. The agent must infer that this is the extraction step of a pipeline purely from the word 'Découpe'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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