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mcp-server-sensory

mcp-server-sensory

by jaspertvdm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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reflux_decode

Decodes SSTV audio to text by converting audio to image and then performing OCR, enabling small language models to process visual information from audio input.

Instructions

Complete REFLUX decode: SSTV Audio → Image → OCR → Text. Gives small LLMs 'eyes' via audio!

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": {
      "audio_base64": "<audio_base64>"
    },
    "name": "reflux_decode"
  }
}

Input Schema

NameRequiredDescriptionDefault
audio_base64YesBase64 encoded SSTV WAV audio

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.1

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. The description mentions a multi-step process (decode, OCR) but does not disclose potential failure modes, required audio format details, data size limits, or whether the operation is read-only or destructive. The phrasing 'Gives small LLMs eyes' is promotional, not informative about behavior.

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

Conciseness5/5

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

The description is extremely concise: two sentences with no wasted words. The first sentence explains the transformation pipeline, the second adds a benefit. Every sentence earns its place.

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?

For a complex multi-step tool with no output schema and no annotations, the description is insufficient. It does not explain the output format (text, metadata), error conditions (invalid audio, unsupported modes), or performance considerations. With only one parameter, the description could have provided more context about expected input characteristics (e.g., audio length, quality).

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 input schema covers 100% of parameters (only one: audio_base64) with a clear description: 'Base64 encoded SSTV WAV audio'. The description adds minimal extra meaning ('SSTV Audio' context) but mostly repeats what the schema already states. Baseline 3 is appropriate given high schema coverage.

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

Purpose5/5

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

The description clearly states the tool's function: 'Complete REFLUX decode: SSTV Audio → Image → OCR → Text.' It specifies the input (SSTV audio) and output (text), and uses a unique verb ('decode') and resource ('REFLUX' for SSTV-to-text pipeline). It distinguishes from sibling tools like sstv_encode_text (which encodes text to SSTV) and morse_decode (Morse code).

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

Usage Guidelines3/5

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

The description implies use for converting SSTV audio to text, but it does not explicitly state when to use this tool versus alternatives. It mentions giving 'small LLMs eyes', which suggests a use case, but no exclusion criteria or alternatives are named. With siblings like sstv_detect and sstv_encode_ponskaart, more guidance would help.

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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