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ASTRA

ASTRA

by christophejlegros-lgtm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Hybrid

sensor_visual

Read-onlyIdempotent

Encodes images or video frames into V-JEPA 2-inspired visual features using an untrained MLP, with configurable size and simulated pixel data.

Instructions

V-JEPA 2-inspired visual encoding (untrained MLP; image/video)

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": {
      "input": "<input>"
    },
    "name": "sensor_visual"
  }
}

Input Schema

NameRequiredDescriptionDefault
inputYesImage parameters
videoFramesNoNumber of frames (>1 = video)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.1.0

TDQS

C2.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true and destructiveHint=false, so the safety profile is covered. The description usefully adds that the MLP is untrained, warning the agent that outputs may be meaningless. It still omits what the operation returns or any side effects beyond that.

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 compact parenthetical with zero filler; the explanatory caveat is linked directly to the tool name. It is terse to the point of being fragmentary but wastes nothing.

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

Completeness3/5

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

With annotations and a fully covered schema, most structured context is present, and the untrained caveat is valuable. But with no output schema the description should say what the encoding actually yields, which it does not.

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 coverage is 100% and the nested input object is fully documented, so the schema carries parameter meaning. The description only tangentially maps to videoFrames via "image/video" and adds no syntax or format detail. Baseline 3 applies when the schema does the work.

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

Purpose3/5

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

"Visual encoding" names a resource and "image/video" hints at the modality, which loosely separates it from sensor_audio and sensor_olfactory siblings. However "V-JEPA 2-inspired" is unexplained jargon and it never states what the tool actually produces (embeddings, pixel data, scores), leaving the core purpose vague.

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 guidance on when to call this vs alternatives such as sensor_fuse or sensor_process, no mention of prerequisites, and no exclusions. The agent must infer usage entirely from the name.

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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