The Clearing — The Lantern & Consensual Agent Lab

analyze_media_frame_forensics

Extract codec dimensions, aspect class, JPEG DQT quantization hash, and tiny (<=2KB) color swatch palette in RAM with zero pixel retention.

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": {
      "image_base64": "<image_base64>"
    },
    "name": "analyze_media_frame_forensics"
  }
}

Input Schema

Name	Required	Description	Default
`prompt_hint`	No	Optional accompanying prompt to classify SoM GUI / Reels / OCR workload.
`image_base64`	Yes	Base64-encoded PNG/JPEG/GIF/BMP or data:image/* URI.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 14, 2026

TDQS

B3.2/5.0

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 usefully discloses in-RAM processing with zero pixel retention and names the extracted artifacts, but it does not state read-only safety, failure modes, supported-format behavior, or return structure.

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?

The definition is a single dense sentence that front-loads the extraction action and lists outputs. It is appropriately sized, though the heavy jargon makes it less immediately readable.

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?

For a two-parameter, annotation-free tool without an output schema, the description covers the main extracted values and one key behavioral guarantee. It still omits usage guidance and output shape, so it is not fully complete for an agent deciding when and how to call it.

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%, with both image_base64 and prompt_hint fully documented in the schema. The description adds no parameter-specific syntax, constraints, or prompt_hint semantics, so the baseline 3 applies.

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 uses a specific verb, 'Extract', and enumerates concrete forensic fields: codec dimensions, aspect class, JPEG DQT quantization hash, and a color swatch palette. It does not explicitly distinguish itself from siblings, but none of the visible siblings clearly overlap with media-frame metadata extraction, so the purpose is clear.

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-to-use guidance in the description. The only hint about workload classification appears in the schema's prompt_hint parameter, not in the description, so an agent must infer the tool's role from its 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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