ASTRA Unified Research Lab MCP Server

sensor_visual

Encodes image or video inputs into V-JEPA 2 visual features for use in neuromorphic simulation pipelines. Handles custom sizes, channels, and simulated frames.

Instructions

V-JEPA 2 Visual Encoding (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

Name	Required	Description	Default
`input`	Yes	Image parameters
`videoFrames`	No	Number of frames (>1 = video)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.0.0Aug 17, 2026

TDQS

C2.4/5.0

Behavior2/5

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only states the model family and input modality, but does not mention whether this is a read-only computation, what side effects exist, what the output representation is, or how parameters such as simulate and videoFrames affect behavior.

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

Conciseness2/5

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

The description is extremely short and contains no filler, but it is under-specified rather than effectively concise. It is a fragment with no sentence structure and no front-loaded action or output expectation.

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 no output schema and no annotations, the description should explain what the encoding returns and how the tool behaves, but it only names the model and modality. The nested input object and the image/video distinction are left entirely to the schema, so an agent cannot reliably understand the result or invocation context.

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?

The schema has 100% parameter documentation coverage, including descriptions for simulate and videoFrames, so the schema carries the explanatory load. The description itself adds no additional parameter meaning beyond the image/video distinction that videoFrames already expresses.

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?

The description identifies this as V-JEPA 2 visual encoding and mentions image/video input, which weakly distinguishes it from sensor_audio and sensor_olfactory. However, it is a noun phrase rather than a clear statement of what the tool does, and it does not specify what kind of output the encoding produces.

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?

The description offers no guidance on when to use this tool versus alternatives such as sensor_process, sensor_fuse, or sensor_status. The intended context is only implied by the word 'visual,' with no exclusions, conditions, or named alternatives.

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

Deploy Server